End-to-end learned Lossy Dynamic Point Cloud Attribute Compression

• URL: http://arxiv.org/abs/2408.10665v1
• Date: Tue, 20 Aug 2024 09:06:59 GMT
• Title: End-to-end learned Lossy Dynamic Point Cloud Attribute Compression
• Authors: Dat Thanh Nguyen, Daniel Zieger, Marc Stamminger, Andre Kaup,
• Abstract summary: This study introduces an end-to-end learned dynamic lossy attribute coding approach. 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.
• Score: 5.717288278431968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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