Graph-based compression of dynamic 3D point cloud sequences (1506.06096v1)

Abstract: This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames are similar, motion estimation is key to effective compression of these sequences. It however remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the points clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.

• The paper introduces a novel graph-based framework that transforms dynamic 3D point cloud sequences into graph signals for efficient motion estimation and compression.
• It estimates motion using spectral graph wavelet descriptors on a sparse set of vertices and refines the results with graph-based quadratic regularization.
• Experimental results show up to 10 dB improvement in coding color information, highlighting the method’s potential for real-time 3D streaming and virtual reality applications.

Graph-Based Compression of Dynamic 3D Point Cloud Sequences

The paper "Graph-based compression of dynamic 3D point cloud sequences" by Dorina Thanou, Philip A. Chou, and Pascal Frossard presents a novel approach to the compression of 3D point cloud sequences. As the interest in dynamic 3D scenes is increasing for applications such as animation, gaming, and virtual reality, efficient compression of these data types has become essential. The challenge lies in the temporal correlation between frames where points can vary without explicit correspondence.

Overview

The authors propose representing the time-varying geometry of these sequences using graphs and treating the 3D positions and color attributes as signals on the vertices of these graphs. This representation transforms the motion estimation problem into a feature matching problem between successive graphs. The approach uses newly developed spectral graph wavelet descriptors to estimate motion on a sparse set of vertices. This sparse motion field is then extended into a dense motion field through graph-based regularization.

Methodology

1. Graph Representation: Each frame in a sequence is represented by a graph where points are vertices, and edges connect neighboring points. This allows the point clouds to be treated as graph signals.
2. Spectral Graph Wavelet Descriptors: To estimate motion, features are computed using spectral graph wavelets—these descriptors capture the local topological context and are used in matching points between frames.
3. Motion Estimation and Regularization: The motion is initially estimated at a sparse set of points and then interpolated to a dense field by solving a graph-based quadratic regularization problem, which ensures smoothness across the graph.
4. Compression Framework: The estimated motion is incorporated into the predictive coding architecture to remove temporal redundancy, using the coded motion vectors to improve the compression efficiency.

Experimental Results and Analysis

Experimental evaluations show that the proposed method effectively estimates motion between consecutive frames. The framework achieves a significant improvement in compression efficiency, particularly in coding color information, with gains up to 10 dB in certain scenarios compared to non-predictive methods. The spatial and temporal correlations captured through the proposed graph-based approach lead to more compact data representation.

Implications and Future Directions

The described method marks a significant step toward efficient compression systems for dynamic 3D content by harnessing graph signal processing principles. From a theoretical standpoint, this creates new opportunities for enhancing data compression through advanced signal processing techniques on graphs. Practically, the approach facilitates applications requiring real-time streaming and storage of 3D content in entertainment and virtual experiences.

For future work, the exploration of lossy compression techniques that leverage motion estimation could be beneficial. Additionally, better integration with machine learning frameworks may optimize the detection of correspondences and refine the graph representation to further increase compactness and reduce computational requirements.

Conclusion

The research presents an innovative solution for compressing dynamic 3D point cloud sequences by leveraging graph-based representations and spectral graph theory. This work provides a foundation upon which further refinement of 3D data handling can be developed, offering potential advancements in real-time applications and improving the feasibility of complex virtual reality systems.