Abstract
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.
Highlights
Vedula et al [1] proposed the scene flow first, which describes a 3D motion field formed by the motion in 3D space scene
Affordable RGBD cameras can directly capture both colour and depth information simultaneously, so we focus on the RGBD scene flow estimation
We propose an assumption of global nonrigid and local rigid motion based on the study of Sun et al [16], which can accurately estimate the motion of each segmented region by dividing each segmented region into different blocks
Summary
Vedula et al [1] proposed the scene flow first, which describes a 3D motion field formed by the motion in 3D space scene. Scene flow is the fundamental input to high-level tasks such as scene understanding and analysis. Affordable RGBD cameras can directly capture both colour and depth information simultaneously, so we focus on the RGBD scene flow estimation. Methods based on segmentation are attractive, which can deal with large displacement and occlusion better. For this method, the correlation of motion in the local area is considered, such as the assumption of local rigid area, which can improve the accuracy of the scene flow estimation. In the local rigid area, it is assumed that all pixels in a segmented region share a rigid motion
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