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29 March 2024 |
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Article overview
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Critical Regularizations for Neural Surface Reconstruction in the Wild | Jingyang Zhang
; Yao Yao
; Shiwei Li
; Tian Fang
; David McKinnon
; Yanghai Tsin
; Long Quan
; | Date: |
7 Jun 2022 | Abstract: | Neural implicit functions have recently shown promising results on surface
reconstructions from multiple views. However, current methods still suffer from
excessive time complexity and poor robustness when reconstructing unbounded or
complex scenes. In this paper, we present RegSDF, which shows that proper point
cloud supervisions and geometry regularizations are sufficient to produce
high-quality and robust reconstruction results. Specifically, RegSDF takes an
additional oriented point cloud as input, and optimizes a signed distance field
and a surface light field within a differentiable rendering framework. We also
introduce the two critical regularizations for this optimization. The first one
is the Hessian regularization that smoothly diffuses the signed distance values
to the entire distance field given noisy and incomplete input. And the second
one is the minimal surface regularization that compactly interpolates and
extrapolates the missing geometry. Extensive experiments are conducted on DTU,
BlendedMVS, and Tanks and Temples datasets. Compared with recent neural surface
reconstruction approaches, RegSDF is able to reconstruct surfaces with fine
details even for open scenes with complex topologies and unstructured camera
trajectories. | Source: | arXiv, 2206.03087 | Services: | Forum | Review | PDF | Favorites |
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