Pointsoup: An Efficient Learning-based Geometry Codec for Compressing Large-Scale Point Cloud Scenes with Extremely Low Decoding Latency

Core Concepts

Pointsoup, an efficient learning-based geometry codec, achieves state-of-the-art compression performance on large-scale point cloud scenes while providing extremely low decoding latency.

Abstract

The paper proposes Pointsoup, an efficient learning-based geometry codec for compressing large-scale point cloud scenes. Key highlights: Pointsoup leverages a point model-based strategy to characterize local surfaces. It embeds skin features from local windows via an attention-based encoder, and introduces dilated windows as cross-scale priors to infer the distribution of quantized features in parallel. During decoding, Pointsoup employs a fast feature refinement block followed by an efficient folding-based point generator to reconstruct the local surface with fast speed. This enables extremely low decoding latency, up to 90-160x faster than the G-PCCv23 Trisoup decoder on a single RTX 2080Ti GPU. Pointsoup achieves state-of-the-art compression performance on multiple benchmarks, providing 60-64% bitrate reduction over the G-PCCv23 anchor. It also offers flexible bitrate control with a lightweight neural model (2.9MB), which is beneficial for practical applications. Experiments show that Pointsoup can effectively handle large-scale point cloud scenes, demonstrating strong generalization capability from a small-scale training dataset.

Stats

Pointsoup achieves up to 90-160x faster decoding than the G-PCCv23 Trisoup decoder on a single RTX 2080Ti GPU. Pointsoup provides 60-64% bitrate reduction over the G-PCCv23 anchor on multiple benchmarks. Pointsoup has a lightweight neural model of only 2.9MB.

Quotes

"Pointsoup achieves state-of-the-art performance on multiple benchmarks with significantly lower decoding complexity, i.e., up to 90∼160× faster than the G-PCCv23 Trisoup decoder on a comparatively low-end platform (e.g., one RTX 2080Ti)." "Furthermore, it offers variable-rate control with a single neural model (2.9MB), which is attractive for industrial practitioners."

Key Insights Distilled From

Pointsoup: High-Performance and Extremely Low-Decoding-Latency Learned Geometry Codec for Large-Scale Point Cloud Scenes

by Kang You,Kai... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13550.pdf

Deeper Inquiries

How can the proposed Pointsoup codec be extended to support other 3D data formats beyond point clouds, such as meshes or voxels

The Pointsoup codec can be extended to support other 3D data formats beyond point clouds by adapting the network architecture and training process to accommodate the specific characteristics of meshes or voxels. For meshes, the codec can incorporate mesh-specific features and structures, such as vertices, edges, and faces, into the encoding and decoding process. This may involve designing new modules to handle mesh connectivity and topology efficiently. Additionally, for voxels, the codec can be modified to work with volumetric data by considering the spatial occupancy of voxels and their relationships.

What are the potential challenges and limitations of the Pointsoup approach when dealing with highly irregular or sparse point cloud scenes

When dealing with highly irregular or sparse point cloud scenes, the Pointsoup approach may face challenges and limitations in accurately capturing and reconstructing the complex geometry. Irregular point distributions can lead to difficulties in feature extraction and representation learning, potentially affecting the compression efficiency and reconstruction quality. Sparse point clouds may result in information loss or distortion during the compression process, especially in regions with limited data points. Maintaining fidelity and detail in such scenarios while ensuring efficient compression remains a significant challenge for the Pointsoup codec.

How can the Pointsoup codec be integrated with other 3D processing tasks, such as object detection or semantic segmentation, to enable end-to-end 3D understanding pipelines

To integrate the Pointsoup codec with other 3D processing tasks like object detection or semantic segmentation, an end-to-end 3D understanding pipeline can be established. The compressed point cloud data from Pointsoup can serve as input to downstream tasks, enabling efficient processing and analysis of 3D scenes. Object detection algorithms can operate directly on the reconstructed point cloud data to identify and localize objects, leveraging the compressed representation for faster inference. Semantic segmentation models can utilize the reconstructed point cloud to assign semantic labels to individual points, facilitating scene understanding and classification. By integrating Pointsoup with these tasks, a comprehensive 3D processing pipeline can be established for various applications in computer vision and robotics.