RGBD Scene Flow Estimation with Global Nonrigid and Local Rigid Assumption

Xiuxiu Li, Yanjuan Liu, Haiyan Jin, Lei Cai, Jiangbin Zheng

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

RGBD scene flow has attracted increasing attention in the computer vision with the popularity of depth sensor. To estimate the 3D motion of object accurately, a RGBD scene flow estimation method with global nonrigid and local rigid motion assumption is proposed in this paper. Firstly, the preprocessing is implemented, which includes the colour-depth registration and depth image inpainting, to processing holes and noises in the depth image; secondly, the depth image is segmented to obtain different motion regions with different depth values; thirdly, scene flow is estimated based on the global nonrigid and local rigid assumption and spatial-temporal correlation of RGBD information. In the global nonrigid and local rigid assumption, each segmented region is divided into several blocks, and each block has a rigid motion. With this assumption, the interaction of motion from different parts in the same segmented region is avoided, especially the nonrigid object, e.g., a human body. Experiments are implemented on RGBD tracking dataset and deformable 3D reconstruction dataset. The visual comparison shows that the proposed method can distinguish the motion parts from the static parts in the same region better, and the quantitative comparisons proved more accurate scene flow can be obtained.

源语言英语
文章编号8215389
期刊Discrete Dynamics in Nature and Society
2020
DOI
出版状态已出版 - 2020

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