Part of schedule#23624
Merged withopencv/opencv_extra#1100
Merged after#23985

This pull request aims to implement whole point cloud compression process, including compression to encode pointcloud data to a compressed binary file, and decompression to recover original pointcloud from it.

Below is the flowchart illustrating the process of point cloud compression.

Below is the visualization of reconstructed pointcloud after decompression.

The point cloud compression and decompression processes can be executed by utilizing the interface of the top-level classPointCloudCompression. User can specify two parameters:

"Resolution" marks the leaf size(least voxel size)of octree. It's related to geometry precision.

"Qstep" is parameter in RAHT algorithm, relates to attribute(color) precision.

(Update): We now add zlib support for Entropy compression and decompression. Since it outperforms out old method, we set it as default algorithm. It can be swapped by changing "codingMethod" initial value in the front of modules/3d/src/pcc.cpp

During the algorithm evaluation, the compression rate was evaluated usingbpp (bits per point), while the reduction quality was measured by means ofPSNR (Peak Signal-to-Noise Ratio).

Note that the calculation ofPSNR involves third-party tool(pc-error from MPEG), thus the function for computingPSNR has not been included in the test file.

The functiongenerateSphere() is used to generate a point cloud file for testing and then fill it with gradient colors. The test pointCloud is a sphere with origin (0,0,0), radius 10, and thickness 0.5. The test results are as follows.

The popular open source point cloud datasetdress_raw.ply is also used for testing. The test results are as follows

Pull Request Readiness Checklist

See details athttps://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

• I agree to contribute to the project under Apache 2 License.
• To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
• The PR is proposed to the proper branch
• There is a reference to the original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake