Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation
Naye Ji, Fan Zhang, Haoxiang Zhang, Youbing Zhao, Dingguo Yu

TL;DR
This paper introduces a GPU-accelerated depth contour occlusion algorithm for mixed reality that improves real-time performance and visual quality of virtual object occlusion by real objects.
Contribution
A novel depth contour occlusion algorithm combining binocular similarity matching and three-dimensional contour optimization, optimized for real-time GPU execution.
Findings
Enhanced real-time performance in mixed reality occlusion
Improved visual quality of real-virtual object integration
Effective handling of complex occlusion scenarios
Abstract
Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of performance and efficiency. To address these challenges, we propose a novel depth contour occlusion (DCO) algorithm. The proposed method is based on the sensitivity of contour occlusion and a binocular stereoscopic vision device. In this method, a depth contour map is combined with a sparse depth map obtained from a two-stage adaptive filter area stereo matching algorithm and the depth contour information of the objects extracted by a digital image stabilisation optical flow method. We also propose a quadratic optimisation model with three constraints to generate an accurate dense map of the depth contour for high-quality real-virtual occlusion. The whole process is accelerated by GPU. To evaluate the…
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Taxonomy
TopicsAdvanced Vision and Imaging · Image and Video Stabilization · Advanced Optical Imaging Technologies
