Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression

Conceitos Básicos

The authors propose PoLoPCAC, an efficient and generic lossless point cloud attribute compression method that achieves high compression efficiency and strong generalizability simultaneously by formulating lossless attribute compression as the task of inferring explicit distributions of attributes from group-wise autoregressive priors.

Resumo

The paper proposes PoLoPCAC, an efficient and generic lossless point cloud attribute compression (PCAC) method. The key highlights are:

1. PoLoPCAC formulates lossless PCAC as the task of inferring explicit distributions of attributes from group-wise autoregressive priors. It uses a progressive random grouping strategy to efficiently resolve the point cloud into groups, and then models the attributes of each group sequentially from accumulated antecedents.

2. A locality-aware attention mechanism is utilized to exploit prior knowledge from context windows in parallel for efficient attribute coding within each group.

3. PoLoPCAC directly operates on points, avoiding distortion caused by voxelization, and can be executed on point clouds with arbitrary scale and density without requiring any prior knowledge of the test domain.

4. Experiments show that PoLoPCAC can be instantly deployed once trained on a small Synthetic 2k-ShapeNet dataset, while enjoying continuous bit-rate reduction over the latest G-PCCv23 on various datasets. It also reports shorter coding time than G-PCCv23 on the majority of sequences with a lightweight model size.

Estatísticas

The paper reports the following key metrics:

Bits per point (Bpp) for various point cloud datasets and compression methods
Encoding and decoding time (in seconds per frame) for different methods

Citações

None

Principais Insights Extraídos De

Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression

by Kang You,Pan... às arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06936.pdf

Perguntas Mais Profundas

How can the proposed group-wise autoregressive modeling be extended to handle dynamic point clouds with temporal coherence

To extend the proposed group-wise autoregressive modeling to handle dynamic point clouds with temporal coherence, we can incorporate a recurrent neural network (RNN) or a transformer architecture. By introducing a temporal dimension to the model, we can capture the sequential nature of dynamic point clouds over time. Each group of points at a specific time step can be treated as a separate input, and the model can learn the temporal dependencies between consecutive groups. This way, the model can leverage the temporal coherence in the point cloud data to improve compression efficiency and generalizability.

What are the potential limitations of the point-based approach, and how can it be further improved to handle extremely sparse or dense point clouds

The point-based approach proposed in the context may face limitations when dealing with extremely sparse or dense point clouds. In sparse point clouds, the model may struggle to capture sufficient contextual information due to the lack of neighboring points. To address this limitation, techniques such as adaptive sampling or attention mechanisms can be incorporated to focus on relevant points and improve information aggregation. In dense point clouds, the model may encounter challenges in processing a large number of points efficiently. To enhance the model's performance with dense point clouds, hierarchical processing or point cloud downsampling techniques can be implemented to reduce the computational complexity while preserving important details.

What other applications beyond lossless attribute compression could benefit from the proposed efficient and generic point model

The proposed efficient and generic point model for lossless attribute compression can be applied to various other applications beyond compression. Some potential applications include:

Point Cloud Denoising: The point model can be utilized for denoising point clouds by learning to reconstruct clean point clouds from noisy input data. The model can effectively remove noise while preserving important features in the point cloud.
Point Cloud Segmentation: The point model can assist in segmenting point clouds into meaningful parts or objects. By learning the underlying structure of the point cloud data, the model can accurately segment different components within the point cloud.
Point Cloud Registration: The point model can aid in point cloud registration tasks by aligning multiple point clouds from different sources or time steps. By understanding the relationships between points in different point clouds, the model can facilitate accurate registration.
Point Cloud Classification: The point model can be used for classifying objects or scenes represented by point clouds. By learning discriminative features from the point cloud data, the model can classify different categories or labels associated with the points.