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Signal Separation Coding for Robust Depth Imaging Based on Structured Light

机译:基于结构光的鲁棒深度成像信号分离编码

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This paper presents an original approach to coding the light patterns for robust depth imaging based on structured light. We have discovered that the degradation of precision and robustness, seen in most conventional approaches to structured light, comes mainly from the overlapping of multiple codes in the signal received at a camera pixel, where the overlapped codes are from the neighbouring and/or, even, distant pixels of the projecting mirror array. Considering the criticality of separating the overlapped codes to precision and robustness, we propose a novel signal separation code, referred to here as “Hierarchical Orthogonal Code (HOC),” for depth imaging. HOC provides not only the separation of overlapped codes, but also a robust decision on pixel correspondence with error correction based on a contextual likelihood among the sets of separated codes from neighbouring camera pixels. The experimental results have shown that the proposed HOC significantly enhances the robustness and precision in depth imaging, compared to the best known conventional approaches. The proposed approach opens a greater feasibility of applying structured light based depth imaging to a 3D modelling of cluttered workspace for home service robots.
机译:本文提出了一种新颖的方法来对光图案进行编码,以用于基于结构化光的鲁棒深度成像。我们已经发现,在大多数传统的结构光方法中,精度和鲁棒性的下降主要来自相机像素接收的信号中多个代码的重叠,其中重叠的代码来自相邻的和/或什至,即投影反射镜阵列的远像素。考虑到分离重叠码对精度和鲁棒性的重要性,我们提出了一种用于深度成像的新型信号分离码,在此称为“ Herarchical Orthogonal Code(HOC)”。 HOC不仅提供了重叠代码的分离,而且还基于与相邻相机像素分离的代码集之间的上下文似然性,通过纠错为像素对应提供了可靠的决策。实验结果表明,与最著名的常规方法相比,所提出的HOC显着提高了深度成像的鲁棒性和精度。所提出的方法为将基于结构化光的深度成像应用于家庭服务机器人的杂乱工作区的3D建模提供了更大的可行性。

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