Merge remote-tracking branch 'upstream/master'

.github/workflows/ubuntu.yml

@@ -12,9 +12,7 @@ jobs:
     runs-on: ubuntu-20.04
     strategy:
       matrix:
-        python-version:
-#          - "3.8"
-          - "3.11"
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - name: Checkout source code
@@ -71,9 +69,8 @@ jobs:
     - name: Publish package
       if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
       env:
-        PYPI_USERNAME: ${{ secrets.pypi_username }}
-        PYPI_PASSWORD: ${{ secrets.pypi_password }}
+        PYPI_API_TOKEN: ${{ secrets.pypi_api_token }}
       run: |
         python -m pip install twine
         cd build/lib/python_package
-        python -m twine upload -u ${PYPI_USERNAME} -p ${PYPI_PASSWORD} pip_package/*
+        python -m twine upload -u "__token__" -p ${PYPI_API_TOKEN} pip_package/*

.github/workflows/windows.yml

@@ -100,8 +100,7 @@ jobs:
       if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
       working-directory: ${{ env.build_dir }}/lib/python_package
       env:
-        PYPI_USERNAME: ${{ secrets.pypi_username }}
-        PYPI_PASSWORD: ${{ secrets.pypi_password }}
+        PYPI_API_TOKEN: ${{ secrets.pypi_api_token }}
       run: |
         python -m pip install twine
-        python -m twine upload -u $env:PYPI_USERNAME -p $env:PYPI_PASSWORD pip_package/*
+        python -m twine upload -u "__token__" -p $env:PYPI_API_TOKEN pip_package/*

Dockerfile

@@ -1,5 +1,5 @@
 
-FROM nvidia/cuda:11.4.0-devel-ubuntu20.04
+FROM nvidia/cuda:11.7.1-devel-ubuntu20.04
 
 WORKDIR /work/cupoch
 
@@ -39,4 +39,5 @@ RUN mkdir build \
 COPY . .
 RUN cd build \
     && cmake .. -DCMAKE_BUILD_TYPE=Release \
-    && make install-pip-package
+    && make pip-package \
+    && pip install lib/python_package/pip_package/*.whl

README.md

@@ -26,6 +26,7 @@ This repository is based on [Open3D](https://github.com/intel-isl/Open3D).
         * [flann](https://github.com/flann-lib/flann)
     * Point cloud registration
         * ICP
+        * [Symmetric ICP](https://gfx.cs.princeton.edu/pubs/Rusinkiewicz_2019_ASO/symm_icp.pdf) (Implemented by [@eclipse0922](https://github.com/eclipse0922))
         * [Colored Point Cloud Registration](https://ieeexplore.ieee.org/document/8237287)
         * [Fast Global Registration](http://vladlen.info/papers/fast-global-registration.pdf)
         * [FilterReg](https://arxiv.org/abs/1811.10136)
@@ -83,7 +84,7 @@ You can also install cupoch using pip on Jetson Nano.
 Please set up Jetson using [jetpack](https://developer.nvidia.com/embedded/jetpack) and install some packages with apt.
 
 ```
-sudo apt-get install libxinerama-dev libxcursor-dev libglu1-mesa-dev
+sudo apt-get install xorg-dev libxinerama-dev libxcursor-dev libglu1-mesa-dev
 pip3 install cupoch
 ```
 
@@ -107,27 +108,6 @@ sudo make install-pip-package
 
 ### Use Docker
 
-Setting default container runtime to nvidia-container-runtime.
-Edit or create the `/etc/docker/daemon.json`.
-
-```sh
-{
-    "runtimes": {
-        "nvidia": {
-            "path": "/usr/bin/nvidia-container-runtime",
-            "runtimeArgs": []
-         }
-    },
-    "default-runtime": "nvidia"
-}
-```
-
-Restart docker daemon.
-
-```sh
-sudo systemctl restart docker
-```
-
 ```sh
 docker-compose up -d
 # xhost +
@@ -147,11 +127,31 @@ The environment tested on has the following specs:
 
 You can get the result by running the example script in your environment.
 
-```
+```sh
 cd examples/python/basic
 python benchmarks.py
 ```
 
+If you get the following error when executing an example that includes 3D drawing, please start the program as follows.
+
+```sh
+$ cd examples/basic
+$ python visualization.py
+Load a ply point cloud, print it, and render it
+MESA: warning: Driver does not support the 0xa7a0 PCI ID.
+libGL error: failed to create dri screen
+libGL error: failed to load driver: iris
+MESA: warning: Driver does not support the 0xa7a0 PCI ID.
+libGL error: failed to create dri screen
+libGL error: failed to load driver: iris
+Error: unknown error	phong_shader.cu:330
+```
+
+```sh
+__NV_PRIME_RENDER_OFFLOAD=1 __GLX_VENDOR_LIBRARY_NAME=nvidia python visualization.py
+```
+
+
 ![speedup](https://raw.githubusercontent.com/neka-nat/cupoch/master/docs/_static/speedup.png)
 
 ### Visual odometry with intel realsense D435

conanfile.py

@@ -27,7 +27,7 @@
 
 class CupochConan(ConanFile):
     name = "cupoch"
-    version = "0.2.8.1"
+    version = "0.2.10.0"
     package_type = "library"
 
     license = "MIT"

src/cupoch/geometry/boundingvolume.cu

@@ -42,7 +42,7 @@ struct check_within_oriented_bounding_box_functor {
     const std::array<Eigen::Vector3f, 8> box_points_;
     __device__ float test_plane(const Eigen::Vector3f &a,
                                 const Eigen::Vector3f &b,
-                                const Eigen::Vector3f c,
+                                const Eigen::Vector3f &c,
                                 const Eigen::Vector3f &x) const {
         Eigen::Matrix3f design;
         design << (b - a), (c - a), (x - a);
@@ -51,7 +51,7 @@ struct check_within_oriented_bounding_box_functor {
     __device__ bool operator()(
             const thrust::tuple<int, Eigen::Vector3f> &x) const {
         const Eigen::Vector3f &point = thrust::get<1>(x);
-        return (test_plane(box_points_[0], box_points_[1], box_points_[3],
+        return ((test_plane(box_points_[0], box_points_[1], box_points_[3],
                            point) <= 0 &&
                 test_plane(box_points_[0], box_points_[5], box_points_[3],
                            point) >= 0 &&
@@ -62,7 +62,19 @@ struct check_within_oriented_bounding_box_functor {
                 test_plane(box_points_[3], box_points_[4], box_points_[5],
                            point) <= 0 &&
                 test_plane(box_points_[0], box_points_[1], box_points_[7],
-                           point) >= 0);
+                           point) >= 0) ||
+                (test_plane(box_points_[0], box_points_[1], box_points_[3],
+                           point) >= 0 &&
+                test_plane(box_points_[0], box_points_[5], box_points_[3],
+                           point) <= 0 &&
+                test_plane(box_points_[2], box_points_[5], box_points_[7],
+                           point) >= 0 &&
+                test_plane(box_points_[1], box_points_[4], box_points_[7],
+                           point) <= 0 &&
+                test_plane(box_points_[3], box_points_[4], box_points_[5],
+                           point) >= 0 &&
+                test_plane(box_points_[0], box_points_[1], box_points_[7],
+                           point) <= 0));
     }
 };
 

src/cupoch/geometry/laserscanbuffer.cu

@@ -298,6 +298,11 @@ template LaserScanBuffer& LaserScanBuffer::AddRanges(
         const Eigen::Matrix4f& transformation,
         const utility::pinned_host_vector<float>& intensities);
 
+template LaserScanBuffer& LaserScanBuffer::AddRanges(
+        const thrust::host_vector<float>& ranges,
+        const Eigen::Matrix4f& transformation,
+        const thrust::host_vector<float>& intensities);
+
 
 class ContainerLikePtr {
 public:

src/cupoch/geometry/occupancygrid.cu

@@ -526,6 +526,17 @@ OccupancyGrid& OccupancyGrid::Insert(
     return Insert(dev_points, viewpoint, max_range);
 }
 
+OccupancyGrid& OccupancyGrid::Insert(
+        const thrust::host_vector<Eigen::Vector3f>& points,
+        const Eigen::Vector3f& viewpoint,
+        float max_range) {
+    utility::device_vector<Eigen::Vector3f> dev_points(points.size());
+    cudaSafeCall(cudaMemcpy(
+            thrust::raw_pointer_cast(dev_points.data()), thrust::raw_pointer_cast(points.data()),
+            points.size() * sizeof(Eigen::Vector3f), cudaMemcpyHostToDevice));
+    return Insert(dev_points, viewpoint, max_range);
+}
+
 OccupancyGrid& OccupancyGrid::Insert(const geometry::PointCloud& pointcloud,
                                      const Eigen::Vector3f& viewpoint,
                                      float max_range) {

src/cupoch/geometry/occupancygrid.h

@@ -108,6 +108,10 @@ class OccupancyGrid : public DenseGrid<OccupancyVoxel> {
             const utility::pinned_host_vector<Eigen::Vector3f>& points,
             const Eigen::Vector3f& viewpoint,
             float max_range = -1.0);
+    OccupancyGrid& Insert(
+            const thrust::host_vector<Eigen::Vector3f>& points,
+            const Eigen::Vector3f& viewpoint,
+            float max_range = -1.0);
     OccupancyGrid& Insert(const PointCloud& pointcloud,
                           const Eigen::Vector3f& viewpoint,
                           float max_range = -1.0);

src/cupoch/integration/scalable_tsdfvolume.cu

@@ -125,8 +125,6 @@ __global__ void OpenVolumeUnitKernel(const Eigen::Vector3f *points,
 struct extract_pointcloud_functor {
     extract_pointcloud_functor(
             const VolumeUnitsMap &volume_units,
-            const stdgpu::device_indexed_range<const VolumeUnitsMap::value_type>
-                    &range,
             int resolution,
             float voxel_length,
             float volume_unit_length,
@@ -135,7 +133,7 @@ struct extract_pointcloud_functor {
             Eigen::Vector3f *normals,
             Eigen::Vector3f *colors)
         : volume_units_(volume_units),
-          range_(range),
+          range_(volume_units.device_range()),
           resolution_(resolution),
           voxel_length_(voxel_length),
           half_voxel_length_(0.5 * voxel_length_),
@@ -363,7 +361,7 @@ std::shared_ptr<geometry::PointCloud> ScalableTSDFVolume::ExtractPointCloud() {
     pointcloud->normals_.resize(3 * n_total, nanvec);
     pointcloud->colors_.resize(3 * n_total, nanvec);
     extract_pointcloud_functor func(
-            impl_->volume_units_, impl_->volume_units_.device_range(),
+            impl_->volume_units_,
             resolution_, voxel_length_, volume_unit_length_, color_type_,
             thrust::raw_pointer_cast(pointcloud->points_.data()),
             thrust::raw_pointer_cast(pointcloud->normals_.data()),