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Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for

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  • 1Electronic Materials Engineering, Kwangwoon University, Kwangwoon-ro 20, Seoul 01897, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel algorithm for compressing dynamic 3D point cloud sequences (PCS) by estimating non-rigid motion. The method enables efficient reconstruction of 3D point clouds (PCFs) by leveraging temporal correlations, similar to 2D video compression.

Keywords:
3D skeletonaugmented realitydeformationdynamic point cloudpose estimationtemporal predictionvirtual reality

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Area of Science:

  • Computer Vision
  • 3D Graphics
  • Data Compression

Background:

  • Dynamic 3D point cloud sequences (PCS) present challenges for efficient transmission and reconstruction.
  • Existing methods often struggle with the non-rigid motion inherent in human or deformable object point clouds.
  • Compression techniques for 3D data lag behind those for 2D imagery.

Purpose of the Study:

  • To develop an algorithm for transmitting and reconstructing dynamic 3D point cloud sequences (PCS) via temporal motion estimation.
  • To enable efficient compression of 3D point cloud frames (PCFs) by adapting 2D video compression principles to 3D non-rigid motion.
  • To provide a method for reconstructing target PCFs using a key PCF, motion vectors, and residual point clouds.

Main Methods:

  • The algorithm divides PCS into groups, selects a key PCF, and estimates 3D skeleton motion via 3D pose estimation.
  • Non-rigid point clouds are converted to mesh models, rigged to the 3D skeleton, and deformed to match target PCFs.
  • Residual point clouds are generated, and reconstruction is achieved using the key PCF, motion vector, and residual PC.

Main Results:

  • Experimental results demonstrate the successful compression of 3D point cloud sequences.
  • The proposed method effectively estimates and utilizes non-rigid 3D motion for compression.
  • The algorithm shows potential for high compression efficiency when combined with existing point cloud compression (PCC) techniques.

Conclusions:

  • The proposed algorithm extends 2D motion estimation to 3D non-rigid object motion for effective point cloud compression.
  • It offers a viable approach for compressing dynamic 3D point cloud data, particularly for human motion.
  • Future integration with standards like MPEG PCC could lead to highly efficient 3D data transmission systems.