3D点云语义分割汇总,所有顶会论文以及一些arxiv上的最新论文
参考来源:
1、https://github.com/Yochengliu/awesome-point-cloud-analysis
- Recent papers (from 2017)
dat.
: dataset | cls.
: classification | rel.
: retrieval | seg.
: segmentation
det.
: detection | tra.
: tracking | pos.
: pose | dep.
: depth
reg.
: registration | rec.
: reconstruction | aut.
: autonomous driving
oth.
: other, including normal-related, correspondence, mapping, matching, alignment, compression, generative model...
Statistics: :fire: code is available & stars >= 100 | :star: citation >= 50
[CVPR] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. [tensorflow][pytorch] [cls.
seg.
det.
] :fire: :star:
[CVPR] SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation. [torch] [seg.
oth.
] :star:
[CVPR] ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. [project][git] [dat.
cls.
rel.
seg.
oth.
] :fire: :star:
[CVPR] OctNet: Learning Deep 3D Representations at High Resolutions. [torch] [cls.
seg.
oth.
] :fire: :star:
[ICCV] Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models. [pytorch] [cls.
rel.
seg.
] :star:
[ICCV] 3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds. [code] [seg.
]
[ICCV] 3D Graph Neural Networks for RGBD Semantic Segmentation. [pytorch] [seg.
]
[NeurIPS] PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. [tensorflow][pytorch] [cls.
seg.
] :fire: :star:
[ICRA] Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications. [code] [seg.
aut.
]
[ICRA] SegMatch: Segment based place recognition in 3D point clouds. [seg.
oth.
]
[3DV] SEGCloud: Semantic Segmentation of 3D Point Clouds. [project] [seg.
aut.
] :star:
[CVPR] SPLATNet: Sparse Lattice Networks for Point Cloud Processing. [caffe] [seg.
] :fire:
[CVPR] Attentional ShapeContextNet for Point Cloud Recognition. [cls.
seg.
]
[CVPR] Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling. [code] [cls.
seg.
]
[CVPR] Pointwise Convolutional Neural Networks. [tensorflow] [cls.
seg.
]
[CVPR] SO-Net: Self-Organizing Network for Point Cloud Analysis. [pytorch] [cls.
seg.
] :fire: :star:
[CVPR] Recurrent Slice Networks for 3D Segmentation of Point Clouds. [pytorch] [seg.
]
[CVPR] 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. [pytorch] [seg.
] :fire:
[CVPR] Deep Parametric Continuous Convolutional Neural Networks. [seg.
aut.
]
[CVPR] SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation. [tensorflow] [seg.
] :fire:
[CVPR] Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs. [pytorch] [seg.
] :fire:
[CVPR] Density Adaptive Point Set Registration. [code] [reg.
]
[CVPR] A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds. [seg.
]
[CVPR] PointGrid: A Deep Network for 3D Shape Understanding. [tensorflow] [cls.
seg.
]
[CVPR] Tangent Convolutions for Dense Prediction in 3D. [tensorflow] [seg.
aut.
]
[ECCV] 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation. [seg.
]
[ECCV] Local Spectral Graph Convolution for Point Set Feature Learning. [tensorflow] [cls.
seg.
]
[ECCV] SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters. [tensorflow] [cls.
seg.
]
[ECCV] Fully-Convolutional Point Networks for Large-Scale Point Clouds. [tensorflow] [seg.
oth.
]
[ECCVW] 3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues. [cls.
seg.
]
[NeurIPS] PointCNN: Convolution On X-Transformed Points. [tensorflow][pytorch] [cls.
seg.
] :fire:
[TOG] Point Convolutional Neural Networks by Extension Operators. [tensorflow] [cls.
seg.
]
[SIGGRAPH Asia] Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. [tensorflow] [cls.
seg.
oth.
]
[SIGGRAPH] Learning local shape descriptors from part correspondences with multi-view convolutional networks. [project] [seg.
oth.
]
[MM] RGCNN: Regularized Graph CNN for Point Cloud Segmentation. [tensorflow] [seg.
]
[ICRA] Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping. [seg.
oth.
]
[ICRA] SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. [tensorflow] [seg.
aut.
]
[IROS] Extracting Phenotypic Characteristics of Corn Crops Through the Use of Reconstructed 3D Models. [seg.
rec.
]
[ACCV] Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds). [tensorflow] [seg.
]
cls.
seg.
oth.
] :fire:cls.
seg.
]cls.
seg.
]cls.
seg.
]cls.
seg.
] :fire:seg.
oth.
]dat.
seg.
]seg.
] :fire:cls.
seg.
]seg.
]seg.
]dat.
seg.
]seg.
] :fire:reg.
]cls.
seg.
]seg.
]seg.
]cls.
seg.
]cls.
seg.
]seg.
] :fire:cls.
seg.
] :fire:seg.
]cls.
seg.
oth.
]seg.
]cls.
seg.
]cls.
seg.
]cls.
seg.
]seg.
]cls.
seg.
oth.
]seg.
]det.
seg.
]seg.
]det.
seg.
aut.
]cls.
seg.
]cls.
seg.
] :fire: :star:seg.
]seg.
oth.
]cls.
seg.
]seg.
aut.
]seg.
aut.
]det.
seg.
]det.
seg.
]cls.
seg.
]seg.
]seg.
aut.
]seg.
] [aut.
]cls.
seg.
]cls.
seg.
]cls.
seg.
oth.
][AAAI] Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling. [seg.
cls.
]
[AAAI] PRIN: Pointwise Rotation-Invariant Network. [seg.
cls.
]
[AAAI] JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds. [tensorflow][seg.
][seg.
]
[CVPR] RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. [tensorflow] [seg.
] :fire:
[CVPR] Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. [pytorch] [seg.
]
[CVPR] AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss. [seg.
]
[CVPR] Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. [tensorflow][det.
] :fire:
[CVPR] Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds. [seg.
]
[CVPR] RPM-Net: Robust Point Matching using Learned Features. [code] [seg.
]
[CVPR] PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. [pytorch] [seg.
]
[CVPR] 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. [seg.
]
[CVPR] OccuSeg: Occupancy-aware 3D Instance Segmentation. [seg.
]
[CVPR] Learning to Segment 3D Point Clouds in 2D Image Space. [pytorch] [seg
]
[CVPR] PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. [seg.
]
[CVPR] DualSDF: Semantic Shape Manipulation using a Two-Level Representation. [code] [seg
]
[CVPR] From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks. [tensorflow] ['image-to-point cloud.']
[CVPR] xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. [Segmentation
]
[CVPR] End-to-End 3D Point Cloud Instance Segmentation Without Detection. [Segmentation
]
[CVPR] Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels. [Segmentation
]
[CVPR] SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel. [Segmentation
]
[CVPR] SpSequenceNet: Semantic Segmentation Network on 4D Point Clouds. [Segmentation
]
[ECCV] PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling. [Upsampling
]
[ECCV] Learning Graph-Convolutional Representations for Point Cloud Denoising. [Denoising
]
[ECCV] JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. [code] [Segmentation
]
[ECCV] Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds. [Segmentation
]
[ECCV] Virtual Multi-view Fusion for 3D Semantic Segmentation. [Segmentation
]
[ECCV] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution. [Segmentation
]
[ECCV] Rotation-robust Intersection over Union for 3D Object Detection. [3D IOU
]
[ECCV] Efficient Outdoor 3D Point Cloud Semantic Segmentation for Critical Road Objects and Distributed Contexts. [Segmentation
]
[ECCV] Deep FusionNet for Point Cloud Semantic Segmentation. [code] [Segmentation
]
[ECCV] SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. [code] [seg.
]
[IROS] PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation. [Segmentation
]
[IROS] RegionNet: Region-feature-enhanced 3D Scene Understanding Network with Dual Spatial-aware Discriminative Loss. [Segmentation
]
[IROS] Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation. [Segmentation
]
[ACM MM] Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene. [Understanding
]
[WACV] FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data. [seg.
aut.
]
[WACV] Global Context Reasoning for Semantic Segmentation of 3D Point Clouds. [seg.
]
[BMVC] ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation. [Segmentation
]
[ICRA] DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans. [seg.
]
[Master Thesis] Neighborhood Pooling in Graph Neural Networks for 3D and 4D Semantic Segmentation. ['seg.']
[CVPR] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges. [code] [Segmentation
]
[CVPR] Monte Carlo Scene Search for 3D Scene Understanding. [Understanding
]
[CVPR] AF2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network. [Segmentation
]
[CVPR] Equivariant Point Network for 3D Point Cloud Analysis. [Analysis
]
[CVPR] PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds. [code] [Convolution
]
[CVPR] Point Cloud Instance Segmentation using Probabilistic Embeddings. [Segmentation
] :dancer:
[CVPR] Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation. [Segmentation
]
[CVPR oral] Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos. [pytorch] [Transformer
]
[CVPR] Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion. [Segmentation
]
[CVPR] Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds. [Segmentation
]
[CVPR] PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks. [code] [Upsampling
]
[CVPR] SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation. [Segmentation
] :dancer:
[CVPR] Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models. [Self-Supervised
]
[CVPR] DyCo3D: Robust Instance Segmentation of 3D Point Clouds Through Dynamic Convolution. [code] [Segmentation
]
[CVPR] TearingNet: Point Cloud Autoencoder To Learn Topology-Friendly Representations. [Autoencoder
]
[CVPR] CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation. [Segmentation
]
[CVPR] Point Cloud Upsampling via Disentangled Refinement. [code] [Upsampling
]
[CVPR] Few-shot 3D Point Cloud Semantic Segmentation. [code] [Segmentation
]
[ICCV] DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation. [Segmentation
]
[ICCV] ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation. [Segmentation
]
[ICCV] Learning with Noisy Labels for Robust Point Cloud Segmentation. [code][Segmentation
]
[ICCV] Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. [code][Segmentation
]
[ICCV] Hierarchical Aggregation for 3D Instance Segmentation. [code][Segmentation
]
[ICCV] Point Transformer. [Transformer
]
[ICCV] TempNet: Online Semantic Segmentation on Large-scale Point Cloud Series. [Segmentation
]
[ICCV] Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation. [Segmentation
]
[ICCV] Guided Point Contrastive Learning for Semi-Supervised Point Cloud Semantic Segmentation. [Segmentation
]
[ICCV] Superpoint Network for Point Cloud Oversegmentation.[code] [Segmentation
]
seg.
]seg.
]seg.
]seg.
]cls.
seg.
]cls.
seg.
reg.
]cls.
seg.
det.
]tra.
seg.
det.
]seg.
aut.
]det.
seg.
aut.
]seg.
oth.
aut.
]seg.
]seg.
]