PR-NeuS: A Prior-based Residual Learning Paradigm for Fast Multi-view Neural Surface Reconstruction

• URL: http://arxiv.org/abs/2312.11577v1
• Date: Mon, 18 Dec 2023 09:24:44 GMT
• Title: PR-NeuS: A Prior-based Residual Learning Paradigm for Fast Multi-view Neural Surface Reconstruction
• Authors: Jianyao Xu, Qingshan Xu, Xinyao Liao, Wanjuan Su, Chen Zhang, Yew-Soon Ong, Wenbing Tao
• Abstract summary: We propose a prior-based residual learning paradigm for fast multi-view neural surface reconstruction. Our method only takes about 3 minutes to reconstruct the surface of a single scene, while achieving competitive surface quality.
• Score: 45.34454245176438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural surfaces learning has shown impressive performance in multi-view surface reconstruction. However, most existing methods use large multilayer perceptrons (MLPs) to train their models from scratch, resulting in hours of training for a single scene. Recently, how to accelerate the neural surfaces learning has received a lot of attention and remains an open problem. In this work, we propose a prior-based residual learning paradigm for fast multi-view neural surface reconstruction. This paradigm consists of two optimization stages. In the first stage, we propose to leverage generalization models to generate a basis signed distance function (SDF) field. This initial field can be quickly obtained by fusing multiple local SDF fields produced by generalization models. This provides a coarse global geometry prior. Based on this prior, in the second stage, a fast residual learning strategy based on hash-encoding networks is proposed to encode an offset SDF field for the basis SDF field. Moreover, we introduce a prior-guided sampling scheme to help the residual learning stage converge better, and thus recover finer structures. With our designed paradigm, experimental results show that our method only takes about 3 minutes to reconstruct the surface of a single scene, while achieving competitive surface quality. Our code will be released upon publication.

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