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Modeling-Based Decoding of the Point Pattern for Active Stereo Vision
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AI
DOI：10.1109/TASE.2024.3422424.png)
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Abstract
En 中文
Active stereo vision (ASV) computes the parallax and depth information from the coded structured light patterns. Thus, it could overcome the difficulties of measuring objects without textures and colors. Pattern decoding is used to obtain the indexed points with pixel coordinates from the acquired pattern image for the subsequent stereo matching. However, decoding of the structured light patterns at locations of color crosstalk, specular reflection and occlusion remains challenging. In this paper, we propose a pattern modeling method to decode the structured light point pattern robustly. The mapping model between the points in the designed pattern and the points in the segmented pattern is computed based on the natural neighbor interpolation. Based on the modeled points, the missing points in the segmented pattern are restored and the noise points in the segmented pattern are removed. The robustness is achieved in the sense of hundred percent point segmentation completeness. Due to the hundred percent completeness, the points in the corresponding blocks are matched directly according to their indexes. Experimental results verified the effectiveness of the proposed active stereo vision approach and also proved that the proposed pattern modeling method could solve the segmentation problems caused by specular reflection, color crosstalk and occlusion effectively.
Keywords:
Image color analysis
Pattern matching
Stereo vision
Image segmentation
Reflection
Decoding
Crosstalk
Active stereo vision
coded structured light pattern
pattern modeling
pattern decoding
scattered data interpolation
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