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NeRF-Guided Unsupervised Learning of RGB-D Registration
May 2, 2024, 4:44 a.m. | Zhinan Yu, Zheng Qin, Yijie Tang, Yongjun Wang, Renjiao Yi, Chenyang Zhu, Kai Xu
cs.CV updates on arXiv.org arxiv.org
Abstract: This paper focuses on training a robust RGB-D registration model without ground-truth pose supervision. Existing methods usually adopt a pairwise training strategy based on differentiable rendering, which enforces the photometric and the geometric consistency between the two registered frames as supervision. However, this frame-to-frame framework suffers from poor multi-view consistency due to factors such as lighting changes, geometry occlusion and reflective materials. In this paper, we present NeRF-UR, a novel frame-to-model optimization framework for unsupervised …
abstract arxiv cs.cv differentiable framework ground-truth however nerf paper registration rendering rgb-d robust strategy supervision training truth type unsupervised unsupervised learning view
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