March 14, 2024, 4:45 a.m. | Chensheng Peng, Chenfeng Xu, Yue Wang, Mingyu Ding, Heng Yang, Masayoshi Tomizuka, Kurt Keutzer, Marco Pavone, Wei Zhan

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.08125v1 Announce Type: new
Abstract: Monocular SLAM has long grappled with the challenge of accurately modeling 3D geometries. Recent advances in Neural Radiance Fields (NeRF)-based monocular SLAM have shown promise, yet these methods typically focus on novel view synthesis rather than precise 3D geometry modeling. This focus results in a significant disconnect between NeRF applications, i.e., novel-view synthesis and the requirements of SLAM. We identify that the gap results from the volumetric representations used in NeRF, which are often dense …

abstract advances arxiv challenge cs.cv fields focus geometry modeling nerf neural radiance fields novel results slam synthesis type view

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