Development Status Update

v0.7.0 is a modest release, but it does have some major API changes that I wanted to get out before taking a deep dive into any new features.

Release Highlights

Removal of ChunkPyramid

You might remember that a ChunkPyramid was added in v0.6.0 for supporting chunk downsampling. Well, it's gone now! Instead, you can just use a ChunkMap for everything that the ChunkPyramid used to do. This just means that now chunk storages are keyed on ChunkKey, which contains a level of detail.

As a result, now many of the ChunkMap methods have changed to require an lod: u8 parameter. And the access trait impls have moved onto a wrapper called the ChunkMapLodView. This means you can do all of the normal access trait stuff on a single LOD at a time.

map.lod_view(0).for_each(...);

Along with this change, I made it so that the ChunkDownsampler can support multichannel Src and Dst chunk types. There aren't currently any users of this feature, but it should be as simple as combining some existing ChunkDownsamplers and applying one to each channel.

borrow_channels

After using the new multichannel arrays for some time, it became apparent that we need a more convenient way of interacting with just a subset of channels. Technically, you could use the TransformMap for this, but it wasn't the most ergonomic option. Now, Array has two new methods: borrow_channels and borrow_channels_mut. They can be used like so:

let mut a = Array3x3::fill(extent, (0.0, 0, 'a'));
// Type annotation not required, just used for educational purpose.
let mut borrow: Array3x2<char, f32, &mut [char], &mut [f32]> = a.borrow_channels_mut(|(f, _, c)| (f, c));

And then borrow is a single channel array that can be used like any other. It implements all of the access traits.

Lockstep array iteration and downsampling perf improvements

Some improvements have been made to the Array iteration code. Now, via the ArrayForEach and LockStepArrayForEach types, you can iterate over the strides for two arrays in lockstep. They also support a step parameter which allows for stepping through arrays at different intervals, which is useful when sampling from arrays at different resolutions. These new types have been used to rewrite the PointDownsampler and SdfMeanDownsampler, giving a 2x perf improvement in the bechmarks.

lod_terrain example works with blocky and smooth voxels

Now you can run the lod_terrain example with either blocky or smooth voxels. You just need to provide a CLI argument of "blocky" or "smooth".

Also, all of the examples have moved into their own crate outside of the root workspace, so you need to cd examples before running them. This was done to avoid pulling in bevy as a dev-dependency when building tests and benchmarks.

Other Changes

Additions

• FillExtent trait
• delete and pop methods for ChunkWriteStorage

Modifications

• bug fixes in the clipmap module; several unit tests added
• collision module of the building_blocks_search crate had some API renames
• OctreeSet now respects VisitStatus::Stop in post-order traversals
• OctreeSet method names have a new "fat" vs "thin" leaf concept
• renamed OctreeDBVT to OctreeDbvt

Benchmark Results

These results come from running cargo bench --all on my PC with an Intel i5-4590 3.3 GHz CPU, using the stable rust v1.52 toolchain and thin LTO enabled. All benchmarks are single-threaded.

greedy_quads_terrace/8  time:   [9.4202 us 9.4465 us 9.4744 us]                                    
greedy_quads_terrace/16 time:   [53.374 us 53.545 us 53.735 us]                                    
greedy_quads_terrace/32 time:   [388.80 us 389.58 us 390.43 us]                                    
greedy_quads_terrace/64 time:   [3.0510 ms 3.0583 ms 3.0667 ms]                                    

height_map_plane/8      time:   [725.52 ns 726.56 ns 727.64 ns]                                
height_map_plane/16     time:   [2.8329 us 2.8407 us 2.8496 us]                                 
height_map_plane/32     time:   [11.822 us 11.853 us 11.890 us]                                 
height_map_plane/64     time:   [49.684 us 49.964 us 50.270 us]                                

surface_nets_sine_sdf/8 time:   [15.828 us 15.952 us 16.082 us]                                     
surface_nets_sine_sdf/16                                                                            
                        time:   [166.98 us 167.42 us 167.92 us]
surface_nets_sine_sdf/32                        
                        time:   [1.1799 ms 1.1831 ms 1.1866 ms]
surface_nets_sine_sdf/64                                                                            
                        time:   [9.9171 ms 9.9553 ms 9.9949 ms]

sphere_surface/8        time:   [1.2471 us 1.2492 us 1.2514 us]                              
sphere_surface/16       time:   [11.169 us 11.185 us 11.202 us]                               
sphere_surface/32       time:   [98.618 us 98.776 us 98.944 us]                              

flood_fill_sphere/16    time:   [32.166 us 32.220 us 32.280 us]                                  
flood_fill_sphere/32    time:   [268.58 us 269.05 us 269.52 us]                                 
flood_fill_sphere/64    time:   [1.9838 ms 1.9876 ms 1.9916 ms]                                  

array_for_each/16       time:   [1.7152 us 1.7187 us 1.7226 us]                               
array_for_each/32       time:   [17.812 us 17.880 us 17.947 us]                               
array_for_each/64       time:   [150.12 us 150.82 us 151.61 us]                              

array_for_each_point/16 time:   [2.4739 us 2.4797 us 2.4858 us]                                     
array_for_each_point/32 time:   [22.124 us 22.171 us 22.220 us]                                     
array_for_each_point/64 time:   [192.28 us 192.86 us 193.58 us]                                    

array_for_each_point_and_stride/16                        
                        time:   [3.9797 us 3.9892 us 4.0013 us]
array_for_each_point_and_stride/32                        
                        time:   [35.803 us 35.887 us 35.981 us]
array_for_each_point_and_stride/64                        
                        time:   [304.04 us 304.63 us 305.24 us]

array_point_indexing/16 time:   [10.691 us 10.707 us 10.724 us]                                     
array_point_indexing/32 time:   [100.41 us 100.87 us 101.30 us]                                    
array_point_indexing/64 time:   [835.65 us 837.50 us 839.67 us]                                    

array_copy/16           time:   [1.6250 us 1.6285 us 1.6321 us]                           
array_copy/32           time:   [16.360 us 16.400 us 16.447 us]                           
array_copy/64           time:   [419.59 us 420.45 us 421.35 us]                          

chunk_hash_map_for_each_point/16                        
                        time:   [4.3827 us 4.3922 us 4.4024 us]
chunk_hash_map_for_each_point/32                        
                        time:   [34.692 us 34.820 us 34.960 us]
chunk_hash_map_for_each_point/64                        
                        time:   [278.21 us 279.76 us 281.59 us]

chunk_hash_map_point_indexing/16                        
                        time:   [62.184 us 62.364 us 62.560 us]
chunk_hash_map_point_indexing/32                        
                        time:   [481.83 us 482.77 us 483.86 us]
chunk_hash_map_point_indexing/64                        
                        time:   [3.8085 ms 3.8159 ms 3.8236 ms]

chunk_hash_map_visit_chunks_sparse/128                        
                        time:   [8.4282 us 8.4485 us 8.4701 us]
chunk_hash_map_visit_chunks_sparse/256                        
                        time:   [179.92 us 183.50 us 187.06 us]
chunk_hash_map_visit_chunks_sparse/512                        
                        time:   [1.5100 ms 1.5337 ms 1.5576 ms]

chunk_hash_map_copy/16  time:   [973.24 ns 975.79 ns 978.56 ns]                                    
chunk_hash_map_copy/32  time:   [9.6658 us 9.7510 us 9.8324 us]                                    
chunk_hash_map_copy/64  time:   [82.749 us 83.486 us 84.364 us]                                   

compressible_chunk_map_point_indexing/16                        
                        time:   [72.292 us 72.756 us 73.223 us]
compressible_chunk_map_point_indexing/32                        
                        time:   [547.20 us 548.71 us 550.51 us]
compressible_chunk_map_point_indexing/64                        
                        time:   [4.3927 ms 4.4007 ms 4.4087 ms]


decompress_array_with_bincode_lz4/16                        
                        time:   [13.079 us 13.118 us 13.165 us]
decompress_array_with_bincode_lz4/32                        
                        time:   [98.359 us 98.610 us 98.864 us]
decompress_array_with_bincode_lz4/64                        
                        time:   [817.73 us 821.12 us 824.67 us]

decompress_array_with_fast_lz4/16                        
                        time:   [5.3044 us 5.3199 us 5.3369 us]
decompress_array_with_fast_lz4/32                        
                        time:   [32.948 us 33.013 us 33.082 us]
decompress_array_with_fast_lz4/64                        
                        time:   [261.91 us 263.90 us 265.97 us]

decompress_array_with_bincode_snappy/16                        
                        time:   [20.258 us 20.375 us 20.485 us]
decompress_array_with_bincode_snappy/32                        
                        time:   [100.17 us 100.45 us 100.80 us]
decompress_array_with_bincode_snappy/64                        
                        time:   [886.38 us 893.72 us 902.32 us]

decompress_array_with_fast_snappy/16                        
                        time:   [9.5126 us 9.5294 us 9.5463 us]
decompress_array_with_fast_snappy/32                        
                        time:   [39.019 us 39.109 us 39.216 us]
decompress_array_with_fast_snappy/64                        
                        time:   [289.97 us 290.53 us 291.16 us]


octree_from_array3_sphere/16                        
                        time:   [20.519 us 20.634 us 20.763 us]
octree_from_array3_sphere/32                        
                        time:   [152.69 us 153.64 us 154.54 us]
octree_from_array3_sphere/64                        
                        time:   [1.1479 ms 1.1534 ms 1.1589 ms]

ooctree_from_array3_full/16                                                                             
                        time:   [16.194 us 16.275 us 16.354 us]
octree_from_array3_full/32                                                                            
                        time:   [125.56 us 126.01 us 126.49 us]
octree_from_array3_full/64                                                                             
                        time:   [1.0397 ms 1.0480 ms 1.0567 ms]

octree_visit_branches_and_fat_leaves_of_sphere/16                                                                             
                        time:   [3.2587 us 3.2863 us 3.3133 us]
octree_visit_branches_and_fat_leaves_of_sphere/32                                                                            
                        time:   [31.885 us 32.161 us 32.443 us]
octree_visit_branches_and_fat_leaves_of_sphere/64                                                                             
                        time:   [168.96 us 170.09 us 171.30 us]

octree_visit_branch_and_leaf_nodes_of_sphere/16                                                                             
                        time:   [8.4103 us 8.4526 us 8.4923 us]
octree_visit_branch_and_leaf_nodes_of_sphere/32                                                                            
                        time:   [66.250 us 66.741 us 67.227 us]
octree_visit_branch_and_leaf_nodes_of_sphere/64                                                                             
                        time:   [301.55 us 303.59 us 305.69 us]


point_downsample3/16    time:   [334.91 ns 337.31 ns 339.58 ns]                                  
point_downsample3/32    time:   [2.3564 us 2.3721 us 2.3863 us]                                  
point_downsample3/64    time:   [147.84 us 148.10 us 148.41 us]                                 

sdf_mean_downsample3/16 time:   [6.1857 us 6.1956 us 6.2061 us]                                     
sdf_mean_downsample3/32 time:   [49.107 us 49.221 us 49.350 us]                                     
sdf_mean_downsample3/64 time:   [393.04 us 395.37 us 397.82 us]                                    

sdf_mean_downsample_chunk_map_with_index/1                                                                            
                        time:   [33.108 us 33.344 us 33.586 us]
sdf_mean_downsample_chunk_map_with_index/2                                                                            
                        time:   [77.578 us 78.137 us 78.707 us]
sdf_mean_downsample_chunk_map_with_index/4                                                                            
                        time:   [486.28 us 489.62 us 493.10 us]
sdf_mean_downsample_chunk_map_with_index/8                                                                             
                        time:   [3.7063 ms 3.7229 ms 3.7409 ms]

Development Status Update

Since v0.5.0, my goal has been to provide tools for implementing level of detail (LoD) and using them in building-blocks-editor. This has taken me on an unexpected detour.

For context, the voxel type in building-blocks-editor looks like this:

struct Voxel {
    distance: Sd8,
    type_id: u8,
}

With LoD, my plan was to downsample chunks into a ChunkPyramid (mipmap). I had this prototyped for voxels with just a signed distance component, but this left me wondering what I should do with the type_id. Just ignore it? Certainly it should not be treated the same by the sampler. In fact, I realized that I don't even have a good reason to downsample the type_id at the moment. Generalizing a bit, I realized that when you have multiple voxel components, you often have workflows that only need to sample a subset of components at a time. But when your voxel is a struct like this, you end up loading the entire thing into cache and wasting space. Of course, the lessons of the ECS paradigm and Structure of Arrays (SoA) dawned on me, and I realized that I should be treating the layout of voxel data a bit differently.

And so the main focus of this release has been what I am calling "multichannel" support. Read more below.

Release Highlights

Multichannel All the Things

TL;DR, now the Array type uses an underlying tuple of Channels, each with a separate flat layout. This means that arrays with multiple channels, like Array3x2<A, B> (an array with 3 spatial dimensions and 2 channels), are supported by multiple independent data channels, i.e. (Channel<A>, Channel<B>). Array supports up to 6 channels.

// Manually create channels for educational purpose.
let ch1 = Channel::fill(0, extent.num_points());
let ch2 = Channel::fill('a', extent.num_points());
let array = Array::new(extent, (ch1, ch2));

// Or use a more convenient constructor.
let array = Array3x2::fill(extent, (0, 'a'));

Similarly, ChunkMap leverages this feature of arrays, and there are new types like ChunkHashMap3x2<A, B> and CompressibleChunkMap3x2<A, B> which support the same access patterns as arrays. Here's what it looks like to use a multichannel ChunkMap:

let ambient_values = (0, 'a');
let builder = ChunkMapBuilder3x2::new(CHUNK_SHAPE, ambient_values);
let mut map = builder.build_with_write_storage(
    FastCompressibleChunkStorageNx2::with_bytes_compression(Lz4 { level: 10 }),
);

let iter_extent = Extent3i::from_min_and_shape(Point3i::fill(10), Point3i::fill(80));

assert_eq!(map.get_mut(Point3i::fill(1)), (&mut 0, &mut 'a'));

map.for_each_mut(&iter_extent, |_p, (num, letter)| {
    *num = 1;
    *letter = 'b';
});

let local_cache = LocalChunkCache::new();
let reader = map.reader(&local_cache);
assert_eq!(reader.get(Point3i::fill(1)), (0, 'a'));
assert_eq!(reader.get_ref(Point3i::fill(1)), (&0, &'a'));

reader.for_each(&iter_extent, |_p, (num, letter)| {
    assert_eq!((num, letter), (1, 'b'));
});

As you can see, everything works like it used to, including chunk compression, access traits, and copy_extent. You can even use TransformMap to project your multichannel map to a subset of channels!

let projection = TransformMap::new(&reader, |(num, _): (i32, char)| num);
projection.for_each(&iter_extent, |_p, num| assert_eq!(num, 1));

Level of Detail

As I mentioned in the last release notes, level of detail is an important feature for scaling up voxel rendering solutions to large maps without wasting memory on high-resolution render resources that are far from the camera. As such, this release includes several new tools for implementing LOD.

While LOD support is very new, it might seem a little obtuse. As I continue integrating this code into the building-blocks editor, I will learn more about how the interface should be shaped. For now, the best resource for learning about this LOD code is the lod_terrain example.

ChunkPyramid and ChunkDownsampler

The core of the LOD solution is to support downsampling chunks into lower levels of detail. The ChunkPyramid is where these downsampled chunks can live. It can be thought of like a sparse mipmap; it is essentially just a vector of ChunkMaps. All of the levels have the same chunk shape, but at lower levels of detail, each chunk covers a larger area of the map (hence having a lower resolution).

Pyramids can be used with any ChunkDownsampler implementation; we currently have a PointDownsampler, which very simply takes one point from each 2x2x2 extent, and the SdfMeanDownsampler, which takes the mean of the signed distances in each 2x2x2 extent. However, ChunkPyramid is currently just a single-channel storage, so you can only downsample one channel per pyramid. This may change in the future. There is a provided workaround for downsampling one channel from a multichannel ChunkMap. You just need to provide a closure that wraps chunks in the proper TransformMap projection. There is a test of this in the chunk_pyramid module if you'd like to see how it's done.

OctreeChunkIndex

The recommended way of managing multiresolution data is to have a ChunkPyramid and a corresponding OctreeChunkIndex which tracks the set of chunks using an OctreeSet for every "super chunk." A super chunk is essentially just a chunk of space indexed by a single OctreeSet.

The reason for the index is for speeding up iteration over large regions of space. Rather than taking a large Extent and checking every single overlapping chunk key (requiring a hash), you can iterate over an OctreeSet, requiring only one hash per level of detail. It's very straightforward to construct an index by calling OctreeChunkIndex::index_chunk_map on a ChunkMap.

Sd8 and Sd16 Signed Distance Types

Previously most of the examples in the building-blocks repo used f32 for signed distances. However, much of the dynamic range of f32 is wasted in this particular use case, since samples of an SDF only need to represent the range [-1.0, 1.0] of distances from the isosurface; any samples further away are not used for surface extraction.

So now we have the Sd8 and Sd16 fixed precision data types, capable of representing numbers with precision 2 / 2^8 and 2 / 2^16 respectively. This will save a lot of space over f32s on large voxel maps.

New Examples

LOD Terrain

Now that we have preliminary support for LOD clipmaps, there is an example of this running in Bevy. And Bevy has a new WireframePlugin, which makes this example even cooler to look at. View it here.

Array Texture Materials

A common technique for texturing Minecraft-style voxels is to use a texture atlas in the form of an "array texture." This is essentially just a 3D texture where each layer has the texture for one block type. Thanks to a PR from @Malmz, we now have an example of this!

Quad Mesh UVs

The "array texture" example also brought to light an issue with how UV coordinates were being generated for a Quad. Now Quad::simple_tex_coords has been moved to OrientedCubeFace::simple_tex_coords, and it supports flipping the U or V texture coordinate axes. This is useful for working with different graphics APIs, since OpenGL, Vulkan, and DirectX do not agree on the UV coordinate space.

A* Pathfinding

Thanks to a PR from @siler, the building_blocks_search crate now has an astar_path function for finding the optimal path between two points on a voxel grid.

Other Changes

Additions

• ChunkMapBuilder has become a trait. Feel free to implement your own builder. The provided ChunkMapBuilderNxM works for vanilla Array chunks.
• OctreeSet::add_extent and OctreeSet::subtract_extent. These are useful for efficiently adding or deleting large regions from a chunk index.

Modifications

• For small-key hash maps, the fnv hasher has been replaced with ahash, which is about 30% faster in our benchmarks. This improves random access performance on ChunkMaps and also the overall performance of OctreeSet.
• The methods on SerializableChunks have changed to take an iterator of (key, chunk) pairs during serialization and fill a chunk storage on deserialization
• OctreeSet::empty has become OctreeSet::new_empty and we now also have OctreeSet::new_full
• Access traits implemented on Fn types are now implemented on the Func type, which is just a newtype for wrapping Fn. This was necessary to avoid conflicting implementations.

Removals

• The Chunk type has been replaced with the Chunk trait. If you need extra metadata on your chunk type, you can implement the Chunk trait. In order to use a custom chunk with the CompressibleChunkStorage, you also need to implement the Compression<Data=YourChunk> trait.
• FastChunkCompression is no longer needed and has been removed.

Benchmark Results

These results come from running cargo bench --all on my PC with an Intel i5-4590 3.3 GHz CPU, using the stable rust v1.50 toolchain and LTO enabled. All benchmarks are single-threaded.

greedy_quads_terrace/8  time:   [9.7631 us 9.9729 us 10.384 us]                                    
greedy_quads_terrace/16 time:   [65.749 us 66.223 us 66.648 us]                                    
greedy_quads_terrace/32 time:   [479.88 us 482.00 us 484.19 us]                                    
greedy_quads_terrace/64 time:   [3.7181 ms 3.7299 ms 3.7417 ms]                                    

height_map_plane/8      time:   [513.01 ns 516.43 ns 519.68 ns]                                
height_map_plane/16     time:   [1.7189 us 1.7236 us 1.7291 us]                                 
height_map_plane/32     time:   [7.4892 us 7.5027 us 7.5169 us]                                 
height_map_plane/64     time:   [31.685 us 31.884 us 32.131 us]                                

surface_nets_sine_sdf/8 time:   [14.929 us 15.055 us 15.180 us]                                     
surface_nets_sine_sdf/16                                                                            
                        time:   [156.87 us 157.20 us 157.59 us]
surface_nets_sine_sdf/32                                                                             
                        time:   [1.1492 ms 1.1568 ms 1.1651 ms]
surface_nets_sine_sdf/64                                                                            
                        time:   [9.6957 ms 9.7798 ms 9.8669 ms]

sphere_surface/8        time:   [12.738 us 12.816 us 12.905 us]                              
sphere_surface/16       time:   [103.64 us 104.24 us 105.00 us]                              
sphere_surface/32       time:   [791.39 us 796.47 us 801.70 us]                               

flood_fill_sphere/16    time:   [321.93 us 322.25 us 322.61 us]                                 
flood_fill_sphere/32    time:   [2.1499 ms 2.1569 ms 2.1648 ms]                                  
flood_fill_sphere/64    time:   [15.533 ms 15.647 ms 15.764 ms]                                 

array_for_each_stride/16                                                                             
                        time:   [1.8162 us 1.8305 us 1.8439 us]
array_for_each_stride/32                                                                             
                        time:   [16.931 us 16.960 us 16.994 us]
array_for_each_stride/64                                                                            
                        time:   [154.09 us 155.21 us 156.32 us]

array_for_each_point/16 time:   [2.7757 us 2.7983 us 2.8215 us]                                     
array_for_each_point/32 time:   [25.216 us 25.398 us 25.568 us]                                     
array_for_each_point/64 time:   [204.42 us 205.38 us 206.55 us]                                    

array_for_each_point_and_stride/16                                                                             
                        time:   [3.6217 us 3.6543 us 3.6864 us]
array_for_each_point_and_stride/32                                                                             
                        time:   [33.404 us 33.456 us 33.512 us]
array_for_each_point_and_stride/64                                                                            
                        time:   [304.74 us 306.71 us 308.61 us]

array_point_indexing/16 time:   [4.9270 us 4.9656 us 5.0093 us]                                     
array_point_indexing/32 time:   [45.621 us 45.844 us 46.110 us]                                     
array_point_indexing/64 time:   [402.73 us 405.25 us 408.02 us]                                    

array_copy/16           time:   [2.0235 us 2.0354 us 2.0504 us]                           
array_copy/32           time:   [18.726 us 18.750 us 18.779 us]                           
array_copy/64           time:   [154.43 us 154.61 us 154.81 us]                          

chunk_hash_map_for_each_point/16                                                                             
                        time:   [3.8854 us 3.8886 us 3.8921 us]
chunk_hash_map_for_each_point/32                                                                            
                        time:   [30.728 us 30.814 us 30.909 us]
chunk_hash_map_for_each_point/64                                                                            
                        time:   [246.98 us 248.12 us 249.30 us]

chunk_hash_map_point_indexing/16                                                                            
                        time:   [53.407 us 53.604 us 53.825 us]
chunk_hash_map_point_indexing/32                                                                            
                        time:   [437.93 us 441.17 us 444.57 us]
chunk_hash_map_point_indexing/64                                                                             
                        time:   [3.4068 ms 3.4142 ms 3.4227 ms]

chunk_hash_map_visit_chunks_sparse/128                                                                             
                        time:   [7.4325 us 7.4942 us 7.5567 us]
chunk_hash_map_visit_chunks_sparse/256                                                                            
                        time:   [72.727 us 73.037 us 73.395 us]
chunk_hash_map_visit_chunks_sparse/512                                                                            
                        time:   [1.6484 ms 1.6653 ms 1.6819 ms]

chunk_hash_map_copy/16  time:   [1.6828 us 1.6924 us 1.7037 us]                                    
chunk_hash_map_copy/32  time:   [12.555 us 12.655 us 12.758 us]                                    
chunk_hash_map_copy/64  time:   [106.73 us 107.89 us 109.23 us]                                   

compressible_chunk_map_point_indexing/16                                                                            
                        time:   [57.566 us 57.898 us 58.251 us]
compressible_chunk_map_point_indexing/32                                                                            
                        time:   [482.42 us 486.15 us 489.81 us]
compressible_chunk_map_point_indexing/64                                                                             
                        time:   [3.7446 ms 3.7674 ms 3.7899 ms]

decompress_array_with_bincode_lz4/16                                                                            
                        time:   [13.053 us 13.150 us 13.250 us]
decompress_array_with_bincode_lz4/32                                                                            
                        time:   [96.656 us 97.476 us 98.356 us]
decompress_array_with_bincode_lz4/64                                                                             
                        time:   [813.80 us 820.56 us 827.36 us]

decompress_array_with_fast_lz4/16                                                                             
                        time:   [5.5177 us 5.5565 us 5.5943 us]
decompress_array_with_fast_lz4/32                                                                            
                        time:   [32.380 us 32.426 us 32.477 us]
decompress_array_with_fast_lz4/64                                                                             
                        time:   [253.46 us 254.01 us 254.60 us]

octree_from_array3_sphere/16                                                                             
                        time:   [20.653 us 20.673 us 20.694 us]
octree_from_array3_sphere/32                                                                            
                        time:   [154.54 us 155.86 us 157.27 us]
octree_from_array3_sphere/64                                                                             
                        time:   [1.1856 ms 1.1967 ms 1.2083 ms]

octree_from_array3_full/16                                                                             
                        time:   [15.459 us 15.479 us 15.501 us]
octree_from_array3_full/32                                                                            
                        time:   [122.86 us 122.99 us 123.13 us]
octree_from_array3_full/64                                                                             
                        time:   [990.73 us 991.75 us 992.84 us]

octree_visit_branches_and_leaves_of_sphere/16                                                                             
                        time:   [6.0710 us 6.0827 us 6.0972 us]
octree_visit_branches_and_leaves_of_sphere/32                                                                            
                        time:   [43.059 us 43.208 us 43.387 us]
octree_visit_branches_and_leaves_of_sphere/64                                                                             
                        time:   [145.64 us 145.82 us 146.02 us]

octree_visit_branch_and_leaf_nodes_of_sphere/16                                                                             
                        time:   [14.560 us 14.576 us 14.594 us]
octree_visit_branch_and_leaf_nodes_of_sphere/32                                                                            
                        time:   [85.230 us 85.339 us 85.461 us]
octree_visit_branch_and_leaf_nodes_of_sphere/64                                                                             
                        time:   [310.08 us 310.36 us 310.65 us]

point_downsample3/16    time:   [1.0997 us 1.1035 us 1.1078 us]                                  
point_downsample3/32    time:   [8.3239 us 8.3351 us 8.3484 us]                                  
point_downsample3/64    time:   [64.942 us 65.026 us 65.122 us]                                 

sdf_mean_downsample3/16 time:   [10.986 us 10.998 us 11.012 us]                                     
sdf_mean_downsample3/32 time:   [84.638 us 84.742 us 84.861 us]                                    
sdf_mean_downsample3/64 time:   [668.83 us 669.47 us 670.19 us]                                    

sdf_mean_downsample_chunk_pyramid_with_index/1                                                                            
                        time:   [56.661 us 56.731 us 56.809 us]
sdf_mean_downsample_chunk_pyramid_with_index/2                                                                            
                        time:   [133.42 us 133.57 us 133.73 us]
sdf_mean_downsample_chunk_pyramid_with_index/4                                                                             
                        time:   [826.90 us 827.86 us 829.00 us]
sdf_mean_downsample_chunk_pyramid_with_index/8                                                                            
                        time:   [6.4673 ms 6.4744 ms 6.4824 ms]

Development Status Update

It's been a while since the last release in December. I hope everyone enjoyed their holidays!

Level of Detail Prototype

TL;DR: Check out this video: https://www.youtube.com/watch?v=6vSz86w3Zjk

My recent focus has been on implementing a level of detail scheme for voxel meshes. If you're not familiar with the problem, generating voxel meshes using the same grid resolution everywhere on a very large map, you will quickly run out of GPU resources, and it's very wasteful, because terrain in the distance will only take up a small number of pixels with many many triangles.

At first, I was looking into using adaptive distance fields and a recursive dual contouring algorithm for meshing. After putting a lot of time into this on the adf-experiment branch, I wasn't pleased with the performance; it was significantly slower than the array-based surface_nets meshing. Although I did learn a lot about meshing across varying levels of detail. Perhaps I will come back to this idea later.

My next idea was to use a clipmap of chunks at varying levels of detail, which would allow me to continue meshing arrays. This required a few new features:

1. Chunk downsampling
2. An octree of chunks to do optimal detection of LOD changes as the camera moves
3. Transition meshing on LOD boundaries

(1) and (2) are mostly done, and you can see how they work in the lod-terrain prototype:

https://www.youtube.com/watch?v=6vSz86w3Zjk

The code for this prototype is mostly found here: https://github.com/bonsairobo/lod-terrain

It relies on the experiment-clipmap branch of the building-blocks repo. The transition meshing is yet to be implemented, and so you can occasionally see some hairline cracks in the LOD terrain. Once I'm happy with the prototype, the plan is to integrate this LOD scheme into the building-blocks-editor, then merge the experiment-clipmap branch into main to provide some code to make managing LOD simple.

Release Highlights

Octree Improvements

In order to facilitate the clipmap, many improvements have been made to the OctreeSet data structure. It is now possible to visit sub-octants of full octants with an OctreeVisitor, and the Location type enables a "nested visitor" pattern, which is used to great effect in the clipmap code. This allows you to use one closure to locate a subtree, then run a separate closure on that subtree, like so:

octree.visit_branches_and_leaves(&mut |location: &Location| {
    if some_condition(location) {
        // Found a particular subtree, now narrow the search using a different algorithm.
        location.visit_all_octants(&octree, &mut |sub_location: &Location| {
            do_something(sub_location.octant());

            VisitStatus::Continue
        });

        VisitStatus::ExitEarly
    } else {
        VisitStatus::Continue
    }
});

ArrayN Generic Storage

Arrays can now be created with any kind of slice storage that implements Deref<[T]> or DerefMut<[T]>.

let mut data = [1; 64 * 64 * 64];
let extent = Extent3i::from_min_and_shape(Point3i::ZERO, PointN([64; 3]));
let mut stack_array = Array3::new(extent, &mut data[..]);

More PointN Ops

Point2i and Point3i now support some vectorized logical operations, like Shl, Shr, BitAnd, BitOr, BitXor, Not, etc.

sdfu Integration

The signed_distances module has been superceded by an integration with the sdfu crate. Now you can create complex SDFs and easily sample them:

use building_blocks::core::sdfu;

let sdf = sdfu::Sphere::new(0.45)
        .subtract(sdfu::Box::new(PointN([0.25, 0.25, 1.5])))
        .union_smooth(
            sdfu::Sphere::new(0.3).translate(PointN([0.3, 0.3, 0.0])),
            0.1,
        )
        .union_smooth(
            sdfu::Sphere::new(0.3).translate(PointN([-0.3, 0.3, 0.0])),
            0.1,
        );

Amanatides and Woo Ray Casting

Because it was simple and I thought some people might want to use it, I implemented the algorithm from this paper. Now you can easily traverse a grid along a given ray like so:

let mut traversal = GridRayTraversal3::new(start, velocity);
while some_condition(traversal.current_voxel()) {
    traversal.step();
}

Change Log

Additions

• The traits in sdfu::mathtypes have been implemented for Point3f. sdfu is now the recommended solution for constructing complex SDFs. See the examples.
• GetRef and ForEachRef traits which provide access to &T in a lattice map
• GridRayTraversal2 and GridRayTraversal3 are implementations of the Amanatides and Woo algorithm
• New APIs for octrees:
  • OctreeSet::add_extent (TODO: subtract_extent)
  • OctreeSet::visit_all_octants
  • Octant::visit_self_and_descendants
• von_neumann_flood_fill3 is a slightly optimized 3D flood fill using scanline search
• greedy_quads now supports transparent voxels via the IsOpaque trait
• Several math operations have been implemented for PointN, including
  • Mul<Self> (and made scalar multiplication symmetric)
  • Rem
  • Shl, Shr
  • BitAnd, BitOr, BitXor, Not
  • min_component and max_component (via MinMaxComponent trait)
• PointN::fill lets you construct a point with all dimensions at the same value
• Some new APIs for ChunkMap:
  • visit_chunks
  • visit_mut_chunks
  • visit_occupied_chunks
  • visit_occupied_mut_chunks
  • get_mut_chunk_or_insert_ambient
• CompressibleChunkMap::reader allows for easy construction of a CompressibleChunkMapReader.

Modifications

• ArrayN is now generic over the storage of its values, as long as the storage implements Deref<[T]> or DerefMut<[T]>. This means you can use any kind of slice pointer: &[T], &mut [T], Box<[T]>, Rc<[T]>, Arc<[T]>, Cow<[T]>, etc.
• The (Octant, bool) parameters in OctreeVisitor have been replaced with Location to enable the "nested visitor" pattern
• OctreeSet::visit has been renamed to OctreeSet::visit_branches_and_leaves to differentiate from OctreeSet::visit_all_octants
• ChunkMap can now only be constructed via the ChunkMapBuilder. This was done to ensure that ChunkMap isn't accidentally created with a storage type that doesn't satisfy the ChunkReadStorage or ChunkWriteStorage traits.
• For simplicity and consistency, wherever possible, we now pass PointN as a parameter by move/copy instead of by borrow.
• A MergeStrategy trait has been extracted from greedy_quads in an effort to provide more generic forms of quad merging. This will eventually be used to support certain kinds of voxel lighting algorithms.
• Surface normal estimation in surface_nets has been improved by using bilinear interpolation of the distance field's partial derivatives, based on where the surface positions is located within a voxel.
• The SignedDistance trait is now a supertrait of Into<f32>
• surface_nets now takes a voxel_size parameter to scale the mesh positions

Removals

• The building_blocks_vox crate has been removed. Functionality now lives in building_blocks_storage because it allows us to define the decode_vox method directly on Array3<VoxColor>.
• The building_blocks_image crate has been removed. Functionality now lives in building_blocks_storage because it allows us to impl From<Im> for Array2.
• The building_blocks_procgen crate has been removed. It only had some common signed distance functions, and these have been replaced with the sdfu integration.
• Some ChunkMap methods were deemed unnecessary or trivial:
  • get_chunk_containing_point
  • get_mut_chunk_containing_point
  • get_mut_point_or_insert_chunk_with

Benchmark Results

I'm going to start posting the criterion benchmark results so it's easier to see performance changes between releases.

These results come from running cargo bench --all on my PC with an Intel i5-4590 3.3 GHz CPU, using the stable rust v1.49 toolchain and LTO enabled. All benchmarks are single-threaded.

greedy_quads_terrace/8  time:   [10.854 us 10.879 us 10.908 us]
greedy_quads_terrace/16 time:   [71.589 us 71.679 us 71.768 us]
greedy_quads_terrace/32 time:   [518.32 us 518.98 us 519.69 us]
greedy_quads_terrace/64 time:   [4.1920 ms 4.2008 ms 4.2101 ms]

height_map_plane/8      time:   [435.84 ns 436.48 ns 437.12 ns]
height_map_plane/16     time:   [1.7318 us 1.7340 us 1.7363 us]
height_map_plane/32     time:   [8.4769 us 8.4862 us 8.4960 us]
height_map_plane/64     time:   [31.885 us 31.974 us 32.078 us]

surface_nets_sine_sdf/8 time:   [12.561 us 12.577 us 12.594 us]
surface_nets_sine_sdf/16
                        time:   [147.24 us 147.44 us 147.65 us]
surface_nets_sine_sdf/32
                        time:   [1.0928 ms 1.0946 ms 1.0966 ms]
surface_nets_sine_sdf/64
                        time:   [9.5181 ms 9.5401 ms 9.5633 ms]

sphere_surface/8        time:   [14.841 us 14.856 us 14.874 us]
sphere_surface/16       time:   [114.60 us 114.74 us 114.88 us]
sphere_surface/32       time:   [863.09 us 864.74 us 866.56 us]

flood_fill_sphere/16    time:   [406.85 us 407.23 us 407.62 us]
flood_fill_sphere/32    time:   [2.7514 ms 2.7549 ms 2.7586 ms]
flood_fill_sphere/64    time:   [20.050 ms 20.076 ms 20.102 ms]

array_for_each_stride/16
                        time:   [1.7999 us 1.8022 us 1.8045 us]
array_for_each_stride/32
                        time:   [16.952 us 16.972 us 16.991 us]
array_for_each_stride/64
                        time:   [147.88 us 148.08 us 148.32 us]

array_for_each_point/16 time:   [13.486 us 13.502 us 13.518 us]
array_for_each_point/32 time:   [131.28 us 131.42 us 131.56 us]
array_for_each_point/64 time:   [1.1749 ms 1.1836 ms 1.1924 ms]

array_for_each_point_and_stride/16
                        time:   [3.9392 us 3.9436 us 3.9481 us]
array_for_each_point_and_stride/32
                        time:   [38.403 us 38.442 us 38.482 us]
array_for_each_point_and_stride/64
                        time:   [337.60 us 337.91 us 338.23 us]

array_point_indexing/16 time:   [4.0816 us 4.0862 us 4.0907 us]
array_point_indexing/32 time:   [42.641 us 42.689 us 42.741 us]
array_point_indexing/64 time:   [350.70 us 351.01 us 351.34 us]

array_copy/16           time:   [2.1374 us 2.1396 us 2.1419 us]
array_copy/32           time:   [18.777 us 18.798 us 18.819 us]
array_copy/64           time:   [477.50 us 477.97 us 478.44 us]

chunk_hash_map_for_each_point/16
                        time:   [20.676 us 20.702 us 20.731 us]
chunk_hash_map_for_each_point/32
                        time:   [165.35 us 165.50 us 165.66 us]
chunk_hash_map_for_each_point/64
                        time:   [1.3206 ms 1.3218 ms 1.3231 ms]

chunk_hash_map_point_indexing/16
                        time:   [133.67 us 133.78 us 133.90 us]
chunk_hash_map_point_indexing/32
                        time:   [1.1237 ms 1.1255 ms 1.1277 ms]
chunk_hash_map_point_indexing/64
                        time:   [8.3298 ms 8.3408 ms 8.3521 ms]

chunk_hash_map_copy/16  time:   [1.6897 us 1.6915 us 1.6934 us]
chunk_hash_map_copy/32  time:   [12.277 us 12.292 us 12.306 us]
chunk_hash_map_copy/64  time:   [103.57 us 103.71 us 103.86 us]

compressible_chunk_map_point_indexing/16
                        time:   [134.74 us 134.86 us 134.98 us]
compressible_chunk_map_point_indexing/32
                        time:   [1.1032 ms 1.1041 ms 1.1051 ms]
compressible_chunk_map_point_indexing/64
                        time:   [8.3600 ms 8.3708 ms 8.3820 ms]

decompress_array_with_bincode_lz4/16
                        time:   [28.234 us 28.281 us 28.329 us]
decompress_array_with_bincode_lz4/32
                        time:   [142.74 us 142.93 us 143.13 us]
decompress_array_with_bincode_lz4/64
                        time:   [1.1786 ms 1.1815 ms 1.1847 ms]

decompress_array_with_fast_lz4/16
                        time:   [5.1769 us 5.1860 us 5.1964 us]
decompress_array_with_fast_lz4/32
                        time:   [32.425 us 32.532 us 32.645 us]
decompress_array_with_fast_lz4/64
                        time:   [263.51 us 264.54 us 265.67 us]

octree_from_array3_sphere/16
                        time:   [23.403 us 23.435 us 23.467 us]
octree_from_array3_sphere/32
                        time:   [170.15 us 170.34 us 170.54 us]
octree_from_array3_sphere/64
                        time:   [1.2522 ms 1.2532 ms 1.2544 ms]

octree_from_array3_full/16
                        time:   [16.656 us 16.673 us 16.691 us]
octree_from_array3_full/32
                        time:   [132.79 us 132.94 us 133.10 us]
octree_from_array3_full/64
                        time:   [1.0669 ms 1.0680 ms 1.0692 ms]

octree_visit_all_octants_of_sphere/16
                        time:   [7.7433 us 7.7551 us 7.7673 us]
octree_visit_all_octants_of_sphere/32
                        time:   [63.031 us 63.116 us 63.203 us]
octree_visit_all_octants_of_sphere/64
                        time:   [209.16 us 209.62 us 210.08 us]

octree_visit_all_octant_nodes_of_sphere/16
                        time:   [14.173 us 14.187 us 14.200 us]
octree_visit_all_octant_nodes_of_sphere/32
                        time:   [101.78 us 101.88 us 101.98 us]
octree_visit_all_octant_nodes_of_sphere/64
                        time:   [353.05 us 354.28 us 355.62 us]

This is a really small release that just contains some documentation bug fixes and improvements. No actual API changes, other than relaxed trait bounds, have been added.

ChunkMap Refactor

This release has some exciting changes to the ChunkMap data structure!

ChunkReadStorage and ChunkWriteStorage

Before, we had a single kind of ChunkMap which forced users to adhere to the LRU caching and compression architecture used in the compressible-map crate. This was potentially cumbersome, especially if you were just getting started with the library and you didn't care about compression (yet).

Now the ChunkMap is generic over the chunk storage. This means you are free to customize how you would like to store you chunks by implementing the ChunkReadStorage and ChunkWriteStorage traits. But we also provide some useful default implementations.

The simplest is ChunkHashMap, which just uses a FnvHashMap for the chunk storage. It's quick and simple, but it doesn't provide any kind of compression. You can construct one using the ChunkMapBuilder::build_with_hash_map_storage method.

For more efficient memory usage, we also have the CompressibleChunkStorage. This is mostly a rewrite of the old CompressibleMap data structure to improve random access performance. The compressible-map dependency was removed and the code lifted into building_blocks_storage::chunk_map::storage.

The architecture is familiar: a top tier LruChunkCache stores uncompressed Chunks, while a bottom tier Slab stores the compressed Chunks. But now we only update the LRU order on insertion or an explicit touch_if_cached method. This made a dramatic improvement to random access performance.

In order to use the read-only access traits on a CompressibleChunkMap (a ChunkMap with CompressibleChunkStorage), you must construct a CompressibleChunkMapReader from a LocalChunkCache. This should be familiar. But now instead of having the reader contain a ChunkMap, the ChunkMap contains the reader as it's storage.

ChunkIndexer

Several methods like extent_for_chunk_at_key, chunk_keys_for_extent, chunk_key_containing_point, etc have moved from the ChunkMap to the ChunkIndexer. Every ChunkMap has a public indexer field.

ChunkMapBuilder

Now it's easier to construct ChunkMaps once you've established a chunk shape, ambient value, and default metadata. All of this data is captured by the ChunkMapBuilder, and you can call ChunkMapBuilder::build with any chunk storage in order to create a ChunkMap. This is especially useful for constructing a CompressibleChunkMapReader from a CompressibleChunkMap, since they will use the same builder.

Take a look at the updated docs for the chunk_map module to learn more.

New OctreeSet Node Traversal

By request, we've added some node-based traversal methods to the OctreeSet. The main use case is for traversing two octrees at the same time; visitors could not do this. So now you can use the root_node and get_child methods to manually traverse the tree.

Default Features

I finally figured out how to make features show up in docs.rs! The rule seems to be: docs.rs will only document default features. However, even if you re-export features from sub-crates in a top-level crate and make them default, that doesn't have any affect on what docs.rs does with the sub-crates (duh?). So I had to make sure every crate had the right default features for what I wanted in docs.rs. However, in order for users of the top-level crate to be able to disable those default features of sub-crates, I need the top-level crate to use default-features = false on all of the sub-crate dependencies.

Phew! TL;DR, you can still pick and choose only the features you want by using default-features = false in your Cargo.toml file, but all of the features will show up in docs.rs now.

This is a major version release with some breaking changes, but nothing groundbreaking in terms of new functionality.

If it seems like there is a lull in new features, that's because I'm currently focusing on building new applications on top of building-blocks, which is just one of the fundamental layer of a full stack the comprises a voxel game. Most of these changes were required by a map editor prototype I'm working on. As the applications get more complex, they will inform the design of building-blocks, and that's what drives new features.

Breaking changes:

• The building_blocks_partition crate has been removed! I decided that it made more sense to keep the OctreeSet type in the building_blocks_storage crate. Everything else, including collision algorithms and the OctreeDBVT have moved to the building_blocks_search crate.
• Several APIs from the compressible-map crate have been exposed as methods directly on the ChunkMap, some have been renamed.
• Some ChunkMap methods now return MaybeCompressedChunk instead of decompressing the chunk for you inline. Just call the as_decompressed method to get the Chunk.
• Renamed Quad to UnorientedQuad.

Additions:

• Added the Snappy compression backend for ChunkMap. To use it, enable the "snappy" feature (and probably disable the "lz4" feature as well). Thanks to @indiv0, who wanted an alternative to LZ4 which works with the wasm target. LZ4 is still recommended as the default for best compression ratios.
• Turned on LTO for release builds and benchmarks, showing a 2x speedup for meshing algorithms. You need to add lto = true to your Cargo.toml to see the improvements.
• Added the Axis2, Axis3, SignedAxis2, and SignedAxis3 types. These get a lot of use in the quad meshing code, where we want to access specific components of Points by indexing instead of doing a dot product.
• Added the ExtentN::from_corners constructor.
• Added the glam feature for easy type conversions between PointN and glam::Vec2 and glam::Vec3.

Other changes:

• Made the code in building_blocks_mesh::quad more reusable for cases outside of the greedy_quads algorithm. Now there is an OrientedCubeFace type and an UnorientedQuad that work together to provide generic quad meshing.

This is a minor release with a few improvements to make v0.2 more usable.

• The docs generated for docs.rs will now use all features.
• The Point2 and Point3 types now support conversion to/from the mint::{Point2, Point3, Vector2, Vector3} types.
• Some parts of the docs around storage have been improved. It should be easier to navigate to the most important pages for getting started.

Building Blocks v0.2.0

This release is all about improving what we already had in v0.1.0, and several breaking changes were necessary. Most notably:

• There is an important bug fix for Octree::from_array3.
• We've added a voxel_sphere_cast function that's similar to voxel_ray_cast.
• All of the meshing functions now support TransformMap.
• We've made several API ergonomics improvements.

Most of these changes have been driven by the effort to port the voxel-mapper project from ilattice3 to building-blocks. The completion of that effort confirms that building-blocks is viable for an application which enables interactive voxel terraforming.

What's Next

We will shift focus to adding new features in the next release. The choice and design of the features will be driven by real use cases. We expect to work on:

• procedurally generating large voxel terrains
• procedurally generating large height map terrains
• real-time voxel editing in a Bevy application

Detailed notes

Meta

• started using Github Actions for CI

Core

• add the Point::at trait method for indexing scalar components
• add the Point::map_components method for transforming points component-wise
• add PointN methods:
  • round
  • floor
  • as_2i and as_3i
  • in_pixel and in_voxel

Storage

• remove GetRef and ForEachRef, instead relying on Get and ForEach, which copy values instead of borrowing. This had no noticeable performance impact.
• impl Deref for ChunkMapReader so it has access to the methods of ChunkMap
• make ChunkMap::from_serializable and to_serializable async so chunk compression can happen in parallel
• improve copy_extent performance when copying large extents between two chunk maps
• rename ChunkMap::key_iter to chunk_keys_for_extent
• rename ChunkMap::chunk_key to chunk_key_containing_point
• cleaned up the Array trait by extract an ArrayIndexer trait
• rename Chunk::map to Chunk::array
• export type aliases for chunk map with default empty metadata type ()

Mesh

• make all meshing algorithms compatible with TransformMap<Array>
• replace pos_norm_tex_meshes_from_material_quads function with more focused methods on the QuadGroupMeta object
• remove the material_weights function, this was very application-specific and relatively trivial to implement

Partition

• fixed a bug with Octree::from_array3 that produced incorrect octants; regression test was added
• add a voxel_sphere_cast function, similar to voxel_ray_cast but with a sphere traveling along the ray
• add the "partition" feature to the list of default features
• remove "ncollide" features from the list of default features
• rename nearest_voxel_ray_cast to voxel_ray_cast and adjust the API parameters
• add OctreeDBVT::remove
• Octree::from_array3 now takes an extent instead of forcing the input array to be the correct shape

I'm excited to announce the first release of Building Blocks, an engine-agnostic Rust library focused on voxel game development.

Here's just a glimpse of what's possible. This is the "bevy_meshing" example, which generates meshes on the fly using various shapes and algorithms:

This is the same core technology behind the voxel-mapper project, which is currently based on the deprecated ilattice3. After a while piling new code into those projects, I decided I was in need of a "new set of bones" with some performance and API improvements.

After a lot of exploration, trials, errors, and lessons learned, I believe I've distilled a useful feature set for developers who need to work with volumetric data in a real-time setting. The most common problems I've encountered when working with billions of voxels are related to performance and memory usage. It has been a fun challenge to craft an ergonomic API that doesn't compromise performance where it really counts. Even with all of the hard work that went into make this library usable, there is still so much room for optimization.

The specific feature set provided by this release is mostly focused on the core data types and containers for voxel data, as well as some meshing, search, and collision algorithms that run on the CPU (GPU acceleration is on the road map). More specifically, you will find:

• the core 2D and 3D point and extent data types used for addressing voxel storage
  • no mandatory dependence on a 3rd party math library like nalgebra, but there are conversion trait impls behind a feature flag
• dense and sparse voxel storage
  • 2D and 3D arrays
  • chunked arrays that support fast compression and LRU caching
    • for the simple kinds of voxels you see in Minecraft, LZ4 chunk compression can achieve an efficiency of 60:1
    • this could theoretically support 125 billion 2-byte voxels in 4 GB of memory
    • LZ4 decompression rates exceed 2 GB/s per core in the worst case
  • fast, composable access traits for indexing, iteration, and copying
  • compressed bincode serialization to save your voxel maps to files or send them over a network
• meshing algorithms that produce coherent meshes, even on chunk boundaries
  • a Naive Surface Nets implementation for smooth voxels
  • a Greedy Meshing implementation for cube voxels
  • an algorithm for meshing height maps
  • all single-threaded, but you can easily run these algorithms on many chunks in parallel using your preferred runtime
• octree acceleration structures for spatial queries
  • very low memory usage (< 1 bit per voxel)
  • supports raycasting against voxel bounding boxes
  • ncollide3d integration behind a feature flag
• pathfinding algorithms
  • greedy best-first search
• criterion benchmarks
  • it is nontrivial to implement real-time voxel algorithms, so there is a suite of benchmarks written using the criterion crate to ensure that we aren't blindly micro-optimizing
• miscellaneous
  • encoding/decoding 3D arrays in VOX format via the dot_vox crate
    • this allows you to import/export your maps for editing in applications like Magicavoxel
  • encoding/decoding 2D arrays into pixel images via the image crate
    • so you can load up height maps from noise images if you like

One notable disclaimer, making a voxel game at massive scale (hundreds of billions of voxels) requires one very important feature that isn't implemented yet: dynamic level of detail. Without it, your meshes will not only look bad at a distance, but they will take up way too much memory. But this item is on the roadmap!

I hope to see more people building cool stuff with this library. Stay tuned for future releases, and please feel welcome to contribute when you have a problem that isn't solved by Building Blocks.

P.S. thanks to @superdump (Rob Swain) and @dekuraan (Declan Li-Carney) for being early adopters and providing me with feedback along the way. You can see one example of building-blocks being used in the wild here.