Comments on "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform"
The recently introduced coder based on region-adaptive hierarchical transform (RAHT) for the compression of point clouds attributes, was shown to have a performance competitive with the state-of-the-art, while being much less complex. In the paper "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform", top performance was achieved using arithmetic coding (AC), while adaptive run-length Golomb-Rice (RLGR) coding was presented as a lower-performance lower-complexity alternative. However, we have found that by reordering the RAHT coefficients we can largely increase the runs of zeros and significantly increase the performance of the RLGR-based RAHT coder. As a result, the new coder, using ordered coefficients, was shown to outperform all other coders, including AC-based RAHT, at an even lower computational cost. We present new results and plots that should enhance those in the work of Queiroz and Chou to include the new results for RLGR-RAHT. We risk to say, based on the results herein, that RLGR-RAHT with sorted coefficients is the new state-of-the-art in point cloud compression.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Rate-Distortion Optimized Skip Coding of Region Adaptive Hierarchical Transform Coefficients for MPEG G-PCC
Three-dimensional (3D) point clouds are becoming more and more popular for representing 3D objects and scenes. Due to limited network bandwidth, efficient compression of 3D point clouds is crucial. To tackle this challen…
Fractional Motion Estimation for Point Cloud Compression
Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes of dynamic 3D point clouds. Our propose…
Motion CompensationMotion Estimation4DAC: Learning Attribute Compression for Dynamic Point Clouds
With the development of the 3D data acquisition facilities, the increasing scale of acquired 3D point clouds poses a challenge to the existing data compression techniques. Although promising performance has been achieved…
AttributeData CompressionMotion CompensationMotion EstimationDALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression
Recently, deep learning has significantly advanced the performance of point cloud geometry compression. However, the learning-based lossless attribute compression of point clouds with varying densities is under-explored.…
Point CloudsCylindrical coordinates for LiDAR point cloud compression
We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles. Due to the circular scanning trajectory of sensors, the geometry of LiDAR point clou…
AttributeAutonomous Vehicles