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CVPR2021顶会

NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning

Hyunho Ha, Joo Ho Lee, Andreas Meuleman, Min H. Kim

2021年份
1顶会引用

摘要

Multiview shape-from-shading (SfS) has achieved highdetail geometry, but its computation is expensive for solving a multiview registration and an ill-posed inverse rendering problem. Therefore, it has been mainly used for offline methods. Volumetric fusion enables real-time scanning using a conventional RGB-D camera, but its geometry resolution has been limited by the grid resolution of the volumetric distance field and depth registration errors. In this paper, we propose a real-time scanning method that can acquire high-detail geometry by bridging volumetric fusion and multiview SfS in two steps. First, we propose the first real-time acquisition of photometric normals stored in texture space to achieve high-detail geometry. We also introduce geometry-aware texture mapping, which progressively refines geometric registration between the texture space and the volumetric distance field by means of normal texture, achieving real-time multiview SfS. We demonstrate our scanning of high-detail geometry using an RGB-D camera at ∼20 fps. Results verify that the geometry quality of our method is strongly competitive with that of offline multiview SfS methods.

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