Nanoflann Versions Save

nanoflann 1.3.2: Released Nov 5, 2020

• Add optional argument for Eigen matrix layout commit.
• Throw exception on malloc failure PR #126.
• Respect GNUInstallDirs in CMake install rules PR #131.

nanoflann 1.3.1: Released Oct 11, 2019

• Fixed bug in KDTreeSingleIndexDynamicAdaptor. See: https://github.com/jlblancoc/nanoflann/commit/a066148517d16c173954dcde13c1527481b9fad3
• Fix build in XCode.
• Simplify CMakeLists for Eigen example (requires Eigen3Config.cmake now)
• Avoid setting cmake global executable build path

Changelog:

• Instructions for make install for Linux and Windows (Closes #87).
• Fix all (?) MSVC conversion warnings (Closes: #95).
• Avoid need for _USE_MATH_DEFINES in MSVC (Closes: #96)
• Eigen::Matrix datasets: now uses std::cref() to store a reference to matrix.
• GSOC2017 contributions by Pranjal Kumar Rai:
  • Support for dynamic datasets.
  • Support for non-Euclidean spaces: SO(2), SO(3)

nanoflann 1.2.3: Released Dec 20, 2016

• Fixed: split plane now correctly chooses the dimensions with the largest span. Should lead to more optimal trees.

nanoflann 1.2.2: Released Nov 10, 2016

• knnSearch() now also returns the number of valid points found.

nanoflann 1.2.1: Released Jun 1, 2016

• Fix potential compiler warnings if IndexType is signed.
• New unit tests comparing the results to those of brute force search.

Changes:

• Fixed the potential of crashes (and minor performance optimization): many classes constructors get const ref arguments but stored const values.

nanoflann 1.1.9: Released Oct 2, 2015

Changes:

• Added KDTreeSingleIndexAdaptor::radiusSearchCustomCallback() (Based on a suggestion by Yannick Morin-Rivest)
• Better documentation in class headers.
• Cleanup of unused code.
• Parameter KDTreeSingleIndexAdaptorParams::dim has been removed since it was redundant.

nanoflann 1.1.8: Released May 2, 2014

• Created hidden constructors in nanoflann class, to disallow unintentional copies which will corrupt the internal pointers.
• Fixed crash if trying to build an index of an empty dataset.