Web: http://arxiv.org/abs/2206.07850

Sept. 23, 2022, 1:15 a.m. | Yiqun Wang, Ivan Skorokhodov, Peter Wonka

cs.CV updates on arXiv.org arxiv.org

Neural rendering can be used to reconstruct implicit representations of
shapes without 3D supervision. However, current neural surface reconstruction
methods have difficulty learning high-frequency geometry details, so the
reconstructed shapes are often over-smoothed. We develop HF-NeuS, a novel
method to improve the quality of surface reconstruction in neural rendering. We
follow recent work to model surfaces as signed distance functions (SDFs).
First, we offer a derivation to analyze the relationship between the SDF, the
volume density, the transparency function, and …


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