Fractional Motion Estimation for Point Cloud Compression

Hong, Haoran; Pavéz, Eduardo; Ortega, Antonio; Watanabe, Ryosuke; Nonaka, Kenichi

doi:10.1109/dcc52660.2022.00045

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Based on this, An et al in [31] enhance the interEM with new block-matching criteria, leading to notable improvements in geometry bitrate gains and coding time efficiency. Moreover, Hong et al in [15] propose a fractional-voxel ME method to accommodate the inherent distinctions between dynamic point clouds. This scheme specifically addresses the irregular distribution of point cloud geometry between consecutive frames.…”

Section: Point Cloud Motion Estimationmentioning

confidence: 99%

“…Dynamic point clouds inherently possess significant temporal redundancy attributed to moving people and objects, often exhibiting regular motion patterns. The primary challenge in dynamic point cloud compression lies in establishing inter-frame point correspondences within unstructured point sets and exploiting temporal correlation to generate a more compact point cloud representation [14][15][16]. It is noteworthy that point correspondences in point cloud sequences are implicit, and the number of points may vary across different frames.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression

Shao,

Gao,

Liu

et al. 2024

Sensors

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The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

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Section: Point Cloud Motion Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%