Neural surface reconstruction aims to reconstruct accurate 3D surfaces based
on multi-view images. Previous methods based on neural volume rendering mostly
train a fully implicit model with MLPs, which typically require hours of
training for a single scene. Recent efforts explore the explicit volumetric
representation to accelerate the optimization via memorizing significant
information with learnable voxel grids. However, existing voxel-based methods
often struggle in reconstructing fine-grained geometry, even when combined with
an SDF-based volume rendering scheme. We reveal that this is because 1) the
voxel grids tend to break the color-geometry dependency that facilitates
fine-geometry learning, and 2) the under-constrained voxel grids lack spatial
coherence and are vulnerable to local minima. In this work, we present Voxurf,
a voxel-based surface reconstruction approach that is both efficient and
accurate. Voxurf addresses the aforementioned issues via several key designs,
including 1) a two-stage training procedure that attains a coherent coarse
shape and recovers fine details successively, 2) a dual color network that
maintains color-geometry dependency, and 3) a hierarchical geometry feature to
encourage information propagation across voxels. Extensive experiments show
that Voxurf achieves high efficiency and high quality at the same time. On the
DTU benchmark, Voxurf achieves higher reconstruction quality with a 20x
training speedup compared to previous fully implicit methods. Our code is
available at this https URL

本文介绍了一种基于体素的表面重建方法 Voxurf，它通过两步训练、双重颜色网络和层次几何特征等关键设计解决了体素网格在重建精细几何以及缺乏空间一致性方面存在的问题，相较于之前的全隐式方法，Voxurf 在训练速度提高 20 倍的同时还能获得更高的重建质量。