DALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression

• URL: http://arxiv.org/abs/2601.12261v1
• Date: Sun, 18 Jan 2026 04:54:46 GMT
• Title: DALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression
• Authors: Chunyang Fu, Ge Li, Wei Gao, Shiqi Wang, Zhu Li, Shan Liu,
• Abstract summary: We develop a learning-based framework, namely DALD-PCAC, to tailor for point cloud attribute compression.<n>We develop a point-wise attention model using a permutation-invariant Transformer to tackle the challenges of sparsity and irregularity of point clouds.<n> Experiments on LiDAR and object point clouds show that DALD-PCAC achieves the state-of-the-art performance on most data.
• Score: 42.588722134560456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. In this paper, we develop a learning-based framework, namely DALD-PCAC that leverages Levels of Detail (LoD) to tailor for point cloud lossless attribute compression. We develop a point-wise attention model using a permutation-invariant Transformer to tackle the challenges of sparsity and irregularity of point clouds during context modeling. We also propose a Density-Adaptive Learning Descriptor (DALD) capable of capturing structure and correlations among points across a large range of neighbors. In addition, we develop a prior-guided block partitioning to reduce the attribute variance within blocks and enhance the performance. Experiments on LiDAR and object point clouds show that DALD-PCAC achieves the state-of-the-art performance on most data. Our method boosts the compression performance and is robust to the varying densities of point clouds. Moreover, it guarantees a good trade-off between performance and complexity, exhibiting great potential in real-world applications. The source code is available at https://github.com/zb12138/DALD_PCAC.

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