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

arXiv: https://arxiv.org/abs/2601.12261 · HTML: https://mlsanity.com/paper/2601.12261/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 71 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [Stratified Transformer for 3D Point Cloud Segmentation](https://mlsanity.com/paper/2203.14508/) (2022)
- [OctFormer: Octree-based Transformers for 3D Point Clouds](https://mlsanity.com/paper/2305.03045/) (2023)
- [Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression](https://mlsanity.com/paper/2111.10633/) (2021)
- [OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression](https://mlsanity.com/paper/2202.06028/) (2022)
- [MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models](https://mlsanity.com/paper/2011.07590/) (2020)
- [Density-preserving Deep Point Cloud Compression](https://mlsanity.com/paper/2204.12684/) (2022)
- [Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds](https://mlsanity.com/paper/1905.03691/) (2019)
- [Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model](https://mlsanity.com/paper/2303.06519/) (2023)
- [3DAC: Learning Attribute Compression for Point Clouds](https://mlsanity.com/paper/2203.09931/) (2022)
- [Multiscale deep context modeling for lossless point cloud geometry compression](https://mlsanity.com/paper/2104.09859/) (2021)
- [RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding](https://mlsanity.com/paper/2206.01738/) (2022)
- [Folding-based compression of point cloud attributes](https://mlsanity.com/paper/2002.04439/) (2020)
- [Attribute Compression of 3D Point Clouds Using Laplacian Sparsity Optimized Graph Transform](https://mlsanity.com/paper/1710.03532/) (2017)
- [Predictive Generalized Graph Fourier Transform for Attribute Compression of Dynamic Point Clouds](https://mlsanity.com/paper/1908.01970/) (2019)
- [Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction](https://mlsanity.com/paper/2303.12917/) (2023)
- [A Volumetric Approach to Point Cloud Compression](https://mlsanity.com/paper/1810.00484/) (2018)
- [Lossless Coding of Point Cloud Geometry using a Deep Generative Model](https://mlsanity.com/paper/2107.00400/) (2021)
- [The JPEG Pleno Learning-based Point Cloud Coding Standard: Serving Man and Machine](https://mlsanity.com/paper/2409.08130/) (2024)
- [ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression](https://mlsanity.com/paper/2211.10916/) (2022)
- [Deep probabilistic model for lossless scalable point cloud attribute compression](https://mlsanity.com/paper/2303.06517/) (2023)
- [No-Reference Point Cloud Quality Assessment via Graph Convolutional Network](https://mlsanity.com/paper/2411.07728/) (2024)
- [Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction](https://mlsanity.com/paper/2106.12236/) (2021)
- [BIRD-PCC: Bi-directional Range Image-based Deep LiDAR Point Cloud Compression](https://mlsanity.com/paper/2303.04027/) (2023)
- [Fast graph-based denoising for point cloud color information](https://mlsanity.com/paper/2401.09721/) (2024)
- [Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences](https://mlsanity.com/paper/2209.08196/) (2022)

## Built on by (most cited first, 1 of 1)

- [OctMesh: A Unified Octree-Hierarchical Framework for Lossless Triangle Mesh Compression](https://mlsanity.com/paper/2610.04281/) (2026)

## Abstract (arXiv)

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.