Photorealistic 3D Mapping of Indoors by RGB-D Scanning Process

Abstract : In this work, a RGB-D input stream is utilized for GPU-boosted 3D reconstruction of textured indoor environments. The goal is to develop a process which produces standard 3D models from indoors to explore them virtually. Camera motion is tracked in 3D space by registering the current view with a reference view. Depending on the trajectory shape, the reference is either fetched from a concurrently built keyframe model or from a previous RGB-D measurement. Real-time tracking (30Hz) is executed on a low-end GPU, which is possible because structural data is not fused concurrently. After camera poses have been estimated, both trajectory and structure are refined in post-processing. The global point cloud is compressed into a watertight polygon mesh by using Poisson reconstruction method. The Poisson method is well-suited, because it compressed the raw data without introducing multiple geometries and also fills holes efficiently. Holes are typically introduced at occluded regions. Texturing is generated by backprojecting the nearest RGB image onto the mesh. The final model is stored in a standard 3D model format to allow easy user exploration and navigation in virtual 3D environment.
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Tommy Tykkala, Andrew I. Comport, Joni-Kristian Kamarainen. Photorealistic 3D Mapping of Indoors by RGB-D Scanning Process. International Conference on Intelligent Robots and Systems, 2013, Tokyo, Japan. ⟨hal-01357355⟩

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