OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression (2202.06028v2)

Abstract: In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention employing the octree structure, a memory-efficient representation for point clouds. Our approach encodes octree symbol sequences in a lossless way by gathering the information of sibling and ancestor nodes. Expressly, we first represent point clouds with octree to reduce spatial redundancy, which is robust for point clouds with different resolutions. We then design a conditional entropy model with a large receptive field that models the sibling and ancestor contexts to exploit the strong dependency among the neighboring nodes and employ an attention mechanism to emphasize the correlated nodes in the context. Furthermore, we introduce a mask operation during training and testing to make a trade-off between encoding time and performance. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset (e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based baseline. The code is available at https://github.com/zb12138/OctAttention.

• The paper presents a novel OctAttention framework that leverages octree representation and attention to efficiently compress sparse point clouds.
• The method aggregates sibling and ancestor node data, achieving 10-35% BD-Rate improvements and over 30% bitrate savings versus state-of-the-art methods.
• The approach reduces encoding time by 95%, highlighting its potential for real-time applications in 3D modeling and autonomous driving.

An Overview of "OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression"

The paper "OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression" introduces a novel framework aimed at improving point cloud compression through the innovative use of octree structures and attention mechanisms. Authored by Chunyang Fu et al., this research addresses the limitations of previous voxel-based methods in handling sparse point clouds by proposing a more sophisticated context modeling approach.

Background and Motivation

Point clouds represent a crucial data structure in 3D modeling used across various fields including autonomous driving and virtual reality. Efficient compression methods are paramount for the storage and transmission of these datasets due to their massive size and unstructured nature. Previous methods, such as voxel-based models, have demonstrated limitations, particularly in scenarios with sparse clouds, due to their restricted receptive fields and computational inefficiency in handling varying densities.

Methodology

The authors propose OctAttention, a multiple-contexts deep learning framework leveraging the memory-efficient octree representation for point clouds. The framework encodes octree symbol sequences losslessly by aggregating information from sibling and ancestor nodes. This approach reduces spatial redundancy and is adaptable to different resolutions. A conditional entropy model with a large receptive field is designed to exploit strong dependencies among neighboring nodes. An attention mechanism further refines the context by emphasizing significantly correlated nodes.

Key aspects of the methodology include:

1. Octree Representation: The authors utilize octrees to overcome the limitations of voxel-based methods, specifically for sparse and variably dense point clouds, by incorporating both sibling and ancestor contexts.
2. Large-Scale Contexts: The proposed model builds on extensive receptive fields by integrating multiple ancestor layers of the point cloud, enabling a better understanding of the data distribution.
3. Attention Mechanism: By employing tree-structured attention, the model identifies and emphasizes important nodes in the context, thereby reducing noise and focusing on dependencies that matter for compression.
4. Mask Operation: This technique allows for parallel encoding, optimizing the trade-off between performance and encoding time, demonstrating a significant improvement over baseline methods during both training and testing.

Results

The OctAttention framework is evaluated on the SemanticKITTI and various object point cloud datasets, such as the MPEG 8i, and produces significant results:

• Achieves a 10-35% BD-Rate improvement compared to state-of-the-art compression methods on LiDAR and object datasets.
• Outperforms the G-PCC standard by more than 30% in average bitrate savings on certain benchmarks.
• Reduces encoding time by 95% relative to voxel-based approaches, highlighting its practical applicability for real-time and efficient point cloud processing.

Implications and Future Directions

The OctAttention model offers a scalable and efficient solution for point cloud compression, particularly suitable for varying densities and resolutions, positioning it as a robust alternative to previous methods. The integration of attention mechanisms within the octree framework could potentially inspire further research into advanced compression strategies leveraging deep learning techniques.

Future work may explore the extension to dynamic point clouds through spatio-temporal modeling or optimizing GPU-based decoding algorithms to enhance real-time applications. Additionally, further refinement in attention scoring and context modeling could yield even more significant improvements in compression efficiency and efficacy.

In summary, this paper presents a substantial contribution to the domain of point cloud compression, providing a framework with both theoretical advancements and practical benefits, suitable for broad application in 3D data handling tasks.