End-to-end learned Lossy Dynamic Point Cloud Attribute Compression
This work addresses a specific bottleneck in point cloud compression for applications like 3D scanning and virtual reality, representing a strong incremental improvement over existing methods.
The paper tackles the problem of attribute compression for dynamic point clouds, which has received less attention than geometry compression, by introducing an end-to-end learned lossy coding approach that achieves 38.1% Bjontegaard Delta-rate savings on average compared to the MPEG standard.
Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with 38.1% Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.