From 3f61cb0c39b195e1f143b072191fffac3c00718b Mon Sep 17 00:00:00 2001 From: koide3 <31344317+koide3@users.noreply.github.com> Date: Sat, 4 May 2024 17:31:57 +0900 Subject: [PATCH] benchmarking the new KdTree (#45) --- BENCHMARK.md | 9 +++++---- README.md | 20 +++++++++++++------- docs/assets/kdtree_time.png | Bin 239925 -> 189313 bytes include/small_gicp/ann/kdtree.hpp | 8 +++----- include/small_gicp/ann/kdtree_omp.hpp | 8 +++----- include/small_gicp/ann/kdtree_tbb.hpp | 8 +++----- scripts/plot_kdtree.py | 8 ++++---- scripts/run_kdtree_benchmark.sh | 2 +- 8 files changed, 32 insertions(+), 31 deletions(-) diff --git a/BENCHMARK.md b/BENCHMARK.md index 78e70d5..452c2be 100644 --- a/BENCHMARK.md +++ b/BENCHMARK.md @@ -48,7 +48,7 @@ All benchmarks were conducted on the KITTI 00 sequence. ```bash cd small_gicp/scripts -./run_downsampling_benchmark.sh +./run_downsampling_benchmark.sh /path/to/kitti/velodyne python3 plot_downsampling.py ``` @@ -67,12 +67,13 @@ python3 plot_downsampling.py ```bash cd small_gicp/scripts -./run_kdtree_benchmark.sh +./run_kdtree_benchmark.sh /path/to/kitti/velodyne python3 plot_kdtree.py ``` - Multi-threaded implementation (TBB and OMP) can be up to **4x faster** than the single-threaded one (All the implementations are based on nanoflann). -- The processing speed gets faster as the number of threads increases, but the speed gain is not monotonic sometimes (because of the scheduling algorithm or some CPU(AMD 5995WX)-specific issues?). +- ~~The processing speed gets faster as the number of threads increases, but the speed gain is not monotonic sometimes (because of the scheduling algorithm or some CPU(AMD 5995WX)-specific issues?)~~. +- The new KdTree implementation shows a good scalability thanks to its well balanced task assignment. - This benchmark only compares the construction time (query time is not included). ![kdtree_time](docs/assets/kdtree_time.png) @@ -81,7 +82,7 @@ python3 plot_kdtree.py ```bash cd small_gicp/scripts -./run_odometry_benchmark.sh +./run_odometry_benchmark.sh /path/to/kitti/velodyne python3 plot_odometry.py ``` diff --git a/README.md b/README.md index fac8bdb..91a437f 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,11 @@ **small_gicp** is a header-only C++ library that offers efficient and parallelized algorithms for fine point cloud registration (ICP, Point-to-Plane ICP, GICP, VGICP, etc.). It is a refined and optimized version of its predecessor, [fast_gicp](https://github.com/SMRT-AIST/fast_gicp), re-written from scratch with the following features. -- **Highly Optimized** : The implementation of the core registration algorithm is further optimized from that in fast_gicp. It enables up to **2x speed gain** compared to fast_gicp. -- **All parallerized** : small_gicp offers parallelized implementations of several preprocessing algorithms to make the entire registration process parallelized (Downsampling, KdTree construction, Normal/covariance estimation). As a parallelism backend, either (or both) [OpenMP](https://www.openmp.org/) and [Intel TBB](https://github.com/oneapi-src/oneTBB) can be used. -- **Minimum dependency** : Only [Eigen](https://eigen.tuxfamily.org/) (and bundled [nanoflann](https://github.com/jlblancoc/nanoflann) and [Sophus](https://github.com/strasdat/Sophus)) are required at a minimum. Optionally, it provides the [PCL](https://pointclouds.org/) registration interface so that it can be used as a drop-in replacement in many systems. +- **Highly Optimized** : The implementation of the core registration algorithm is further optimized from that in fast_gicp. It enables up to **2x speed gain**. +- **All parallerized** : small_gicp offers parallel implementations of several preprocessing algorithms to make the entire registration process parallelized (Downsampling, KdTree construction, Normal/covariance estimation). As a parallelism backend, either (or both) [OpenMP](https://www.openmp.org/) and [Intel TBB](https://github.com/oneapi-src/oneTBB) can be used. +- **Minimum dependency** : Only [Eigen](https://eigen.tuxfamily.org/) (and bundled [nanoflann](https://github.com/jlblancoc/nanoflann) and [Sophus](https://github.com/strasdat/Sophus)) are required at a minimum. Optionally, it provides the [PCL](https://pointclouds.org/) registration interface so that it can be used as a drop-in replacement. - **Customizable** : small_gicp allows feeding any custom point cloud class to the registration algorithm via traits. Furthermore, the template-based implementation enables customizing the registration process with your original correspondence estimator and registration factors. -- **Python bindings** : The isolation from PCL makes small_gicp's python bindings more portable and connectable to other libraries (e.g., Open3D) without problems. +- **Python bindings** : The isolation from PCL makes small_gicp's python bindings more portable and usable with other libraries (e.g., Open3D) without problems. Note that GPU-based implementations are NOT included in this package. @@ -22,7 +22,7 @@ This library uses some C++17 features. The PCL interface is not compatible with ## Dependencies - [Mandatory] [Eigen](https://eigen.tuxfamily.org/), [nanoflann](https://github.com/jlblancoc/nanoflann) ([bundled](include/small_gicp/ann/kdtree.hpp)), [Sophus](https://github.com/strasdat/Sophus) ([bundled](include/small_gicp/util/lie.hpp)) -- [Optional] [OpenMP](https://www.openmp.org/), [Intel TBB](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onetbb.html), [PCL](https://pointclouds.org/), [Iridescence](https://github.com/koide3/iridescence) +- [Optional] [OpenMP](https://www.openmp.org/), [Intel TBB](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onetbb.html), [PCL](https://pointclouds.org/) ## Installation @@ -385,6 +385,11 @@ open3d.visualization.draw_geometries([target_o3d, source_o3d]) + +### Cookbook + +- [Scan-to-scan and scan-to-model GICP matching odometry on KITTI](src/example/kitti_odometry.py) + ## [Benchmark](BENCHMARK.md) Processing speed comparison between small_gicp and Open3D ([youtube]((https://youtu.be/LNESzGXPr4c?feature=shared))). @@ -401,8 +406,9 @@ Processing speed comparison between small_gicp and Open3D ([youtube]((https://yo ### KdTree construction -- Multi-threaded implementation (TBB and OMP) can be up to **4x faster** than the single-threaded one (All the implementations are based on nanoflann). -- The processing speed gets faster as the number of threads increases, but the speed gain is not monotonic sometimes (because of the scheduling algorithm or some CPU(AMD 5995WX)-specific issues?). +- Multi-threaded implementation (TBB and OMP) can be up to **6x faster** than the single-threaded one. 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