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We introduce chroma subsampling for 3D point cloud attribute compression by proposing a novel technique to sample points irregularly placed in 3D space. While most current video compression standards use chroma subsampling, these chroma subsampling methods cannot be directly applied to 3D point clouds, given their irregularity and sparsity. In this work, we develop a framework to incorporate chroma subsampling into geometry-based point cloud encoders, such as region adaptive hierarchical transform (RAHT) and region adaptive graph Fourier transform (RAGFT). We propose different sampling patterns on a regular 3D grid to sample the points at different rates. We use a simple graph-based nearest neighbor interpolation technique to reconstruct the full resolution point cloud at the decoder end. Experimental results demonstrate that our proposed method provides significant coding gains with negligible impact on the reconstruction quality. For some sequences, we observe a bitrate reduction of 10-15% under the Bjontegaard metric. More generally, perceptual masking makes it possible to achieve larger bitrate reductions without visible changes in quality.Free, publicly-accessible full text available May 23, 2023
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 proposed block-based fractional-voxel motion estimation scheme takes into account the fundamental differences between point clouds and videos, i.e., the irregularity of the distribution of voxels within a frame and across frames. We show that motion compensation can benefit from the higher resolution reference and more accurate displacements provided by fractional precision. Our proposed scheme significantly outperforms comparable methods that only use integer motion. The proposed scheme can be combined with and add sizeable gains to state-of-the-art systems that use transforms such as Region Adaptive Graph Fourier Transform and Region Adaptive Haar Transform.Free, publicly-accessible full text available March 1, 2023
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 clouds is inherently different from that of point clouds captured from RGBD cameras. Our method exploits these specific properties to representing points in cylindrical coordinates instead of conventional Cartesian coordinates. We demonstrate that Region Adaptive Hierarchical Transform (RAHT) can be extended to this setting, leading to attribute encoding based on a volumetric partition in cylindrical coordinates. Experimental results show that our proposed voxelization outperforms conventional approaches based on Cartesian coordinates for this type of data. We observe a significant improvement in attribute coding performance with 5-10% reduction in bitrate and octree representation with 35-45% reduction in bits.