Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression

2025-09-17

AI Summary

Dynamic point cloud compression faces challenges in motion modeling due to irregular spatial structures and strong temporal variations; existing explicit motion estimation methods struggle to capture complex dynamics and fail to fully exploit temporal correlations. This paper proposes a feature-aligned motion transformation framework: (1) implicit spatiotemporal feature alignment replaces explicit optical flow estimation; (2) a stochastic reference selection strategy enables bidirectional motion referencing and frame-level parallel decoding; and (3) latent-space conditional coding coupled with a hierarchical entropy model enhances rate-distortion performance. Evaluated on standard benchmarks, the method achieves BD-Rate reductions of 20.0% and 9.4% over D-DPCC and AdaDPCC, respectively, demonstrating significant improvements in compression efficiency and decoding throughput.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Dynamic point clouds are widely used in applications such as immersive reality, robotics, and autonomous driving. Efficient compression largely depends on accurate motion estimation and compensation, yet the irregular structure and significant local variations of point clouds make this task highly challenging. Current methods often rely on explicit motion estimation, whose encoded vectors struggle to capture intricate dynamics and fail to fully exploit temporal correlations. To overcome these limitations, we introduce a Feature-aligned Motion Transformation (FMT) framework for dynamic point cloud compression. FMT replaces explicit motion vectors with a spatiotemporal alignment strategy that implicitly models continuous temporal variations, using aligned features as temporal context within a latent-space conditional encoding framework. Furthermore, we design a random access (RA) reference strategy that enables bidirectional motion referencing and layered encoding, thereby supporting frame-level parallel compression. Extensive experiments demonstrate that our method surpasses D-DPCC and AdaDPCC in both encoding and decoding efficiency, while also achieving BD-Rate reductions of 20% and 9.4%, respectively. These results highlight the effectiveness of FMT in jointly improving compression efficiency and processing performance.

Problem

• Replacing explicit motion estimation with implicit spatiotemporal alignment
• Addressing irregular structure challenges in dynamic point cloud compression
• Enhancing temporal correlation exploitation through feature-aligned transformation

Innovation

• Feature-aligned implicit motion transformation
• Latent-space conditional encoding framework
• Bidirectional layered parallel compression