setup.py

CMakeLists.txt

@@ -79,16 +79,18 @@ endif()
 find_package(Python COMPONENTS Interpreter Development)
 find_package(pybind11 CONFIG)
 
-# Python binding
-pybind11_add_module(small_gicp src/python/python.cpp)
-target_include_directories(small_gicp PUBLIC
-  include
-  ${EIGEN3_INCLUDE_DIR}
-)
-target_link_libraries(small_gicp PRIVATE
-  small_gicp_helper
-  OpenMP::OpenMP_CXX
-)
+# Python bindings
+if(BUILD_PYTHON_BINDINGS)
+  pybind11_add_module(small_gicp src/python/python.cpp)
+  target_include_directories(small_gicp PUBLIC
+    include
+    ${EIGEN3_INCLUDE_DIR}
+  )
+  target_link_libraries(small_gicp PRIVATE
+    small_gicp_helper
+    OpenMP::OpenMP_CXX
+  )
+endif()
 
 ###############
 ## Benchmark ##
@@ -108,7 +110,8 @@ if(BUILD_BENCHMARKS)
     add_compile_definitions(BUILD_WITH_IRIDESCENCE)
   endif()
   if (BUILD_WITH_FAST_GICP)
-    set(FAST_GICP_INCLUDE_DIR /home/koide/workspace/fast_gicp/include)
+    # set(FAST_GICP_INCLUDE_DIR /home/koide/workspace/fast_gicp/include)
+    set(FAST_GICP_INCLUDE_DIR $ENV{FAST_GICP_INCLUDE_DIR})
     add_compile_definitions(BUILD_WITH_FAST_GICP)
   endif()
 

README.md

@@ -1,12 +1,12 @@
 # small_gicp (fast_gicp2)
 
-**small_gicp** is a header-only C++ library that provides efficient and parallelized fine point cloud registration algorithms (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), with the following features.
+**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 offers up to 2x speed up compared to fast_gicp.
-- **All parallerized** : small_gicp offers parallelized implementations of several algorithms in the point cloud registration process (Downsampling, KdTree construction, Normal/covariance estimation). As a parallelism backend, either (or both) of [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.
-- **Customizable** : small_gicp is implemented with the trait mechanism that allows feeding any custom point cloud class to the registration algorithm. Furthermore, the template-based implementation allows customizing the regisration process with your original correspondence estimator and registration factors.
-- **Python bindinds** (coming soon) : The isolation from PCL makes the small_gicp's python bindinds more portable and connectable to other libraries seamlessly. 
+- **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) of [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 for registration algorithms in PCL.
+- **Customizable** : small_gicp is implemented with the trait mechanism that enables feeding any custom point cloud class to the registration algorithm. Furthermore, the template-based implementation allows customizing the regisration process with your original correspondence estimator and registration factors.
+- **Python bindinds** : The isolation from PCL makes small_gicp's python bindinds more portable and connectable to other libraries seamlessly. 
 
 Note that GPU-based implementations are NOT included in this package.
 

docker/Dockerfile.gcc

@@ -6,11 +6,13 @@ ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get update && apt-get install --no-install-recommends -y \
   && apt-get install --no-install-recommends -y \
-  wget nano build-essential git cmake \
+  wget nano build-essential git cmake python3-pip pybind11-dev \
   libeigen3-dev libfmt-dev libtbb-dev libomp-dev libpcl-dev libgtest-dev  \
   && apt-get clean \
   && rm -rf /var/lib/apt/lists/*
 
+RUN pip install -U setuptools pytest numpy scipy
+
 COPY . /root/small_gicp
 WORKDIR /root/small_gicp/build
 RUN rm -rf ./*
@@ -22,6 +24,10 @@ RUN cmake .. -DBUILD_TESTS=ON -DBUILD_EXAMPLES=ON -DBUILD_BENCHMARKS=ON -DBUILD_
 RUN cmake --build . -j$(nproc)
 RUN ctest -j$(nproc)
 
+WORKDIR /root/small_gicp
+RUN python3 setup.py build && python3 setup.py install
+RUN pytest src/example/basic_registration.py
+
 WORKDIR /
 
 CMD ["bash"]

docker/Dockerfile.llvm

@@ -4,7 +4,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get update && apt-get install --no-install-recommends -y \
   && apt-get install --no-install-recommends -y \
-  wget nano build-essential git cmake \
+  wget nano build-essential git cmake python3-pip pybind11-dev \
   libeigen3-dev libfmt-dev libtbb-dev libomp-dev libpcl-dev libgtest-dev \
   && apt-get clean \
   && rm -rf /var/lib/apt/lists/*
@@ -15,6 +15,8 @@ RUN apt-get update && apt-get install --no-install-recommends -y \
   && apt-get clean \
   && rm -rf /var/lib/apt/lists/*
 
+RUN pip install -U setuptools pytest numpy scipy
+
 RUN update-alternatives --install /usr/bin/ld ld /usr/bin/ld.lld 50
 ENV CC=clang
 ENV CXX=clang++
@@ -30,6 +32,10 @@ RUN cmake .. -DBUILD_TESTS=ON -DBUILD_EXAMPLES=ON -DBUILD_BENCHMARKS=ON -DBUILD_
 RUN cmake --build . -j$(nproc)
 RUN ctest -j$(nproc)
 
+WORKDIR /root/small_gicp
+RUN python3 setup.py build && python3 setup.py install
+RUN pytest src/example/basic_registration.py
+
 WORKDIR /
 
 CMD ["bash"]

setup.py

@@ -45,8 +45,8 @@ def build_extension(self, ext: CMakeExtension) -> None:
         # EXAMPLE_VERSION_INFO shows you how to pass a value into the C++ code
         # from Python.
         cmake_args = [
+            f"-DBUILD_PYTHON_BINDINGS=ON",
             f"-DCMAKE_LIBRARY_OUTPUT_DIRECTORY={extdir}{os.sep}",
-            f"-DPYTHON_EXECUTABLE={sys.executable}",
             f"-DCMAKE_BUILD_TYPE={cfg}",  # not used on MSVC, but no harm
         ]
         build_args = []
@@ -55,9 +55,6 @@ def build_extension(self, ext: CMakeExtension) -> None:
         if "CMAKE_ARGS" in os.environ:
             cmake_args += [item for item in os.environ["CMAKE_ARGS"].split(" ") if item]
 
-        # In this example, we pass in the version to C++. You might not need to.
-        cmake_args += [f"-DEXAMPLE_VERSION_INFO={self.distribution.get_version()}"]
-
         if self.compiler.compiler_type != "msvc":
             # Using Ninja-build since it a) is available as a wheel and b)
             # multithreads automatically. MSVC would require all variables be

src/example/basic_registration.py

@@ -3,7 +3,6 @@
 from scipy.spatial.transform import Rotation
 
 import small_gicp
-from pyridescence import *
 
 
 # Basic registation example with numpy arrays
@@ -97,8 +96,8 @@ def verify_result(T_target_source, gt_T_target_source):
   error_trans = numpy.linalg.norm(error[:3, 3])
   error_rot = Rotation.from_matrix(error[:3, :3]).magnitude()
 
-  assert error_trans < 0.01
-  assert error_rot < 0.01
+  assert error_trans < 0.05
+  assert error_rot < 0.05
 
 import pytest