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 https://github.com/wutong16/Voxurf.

翻译：神经表面重建旨在基于多视图图像重建精确的三维表面。以往基于神经体渲染的方法大多使用多层感知机（MLP）训练一个完全隐式模型，这类方法通常需要数小时才能完成单个场景的训练。近期工作探索了利用可学习的体素网格存储显著信息，通过显式体素表示来加速优化过程。然而，现有的基于体素的方法即使结合带符号距离函数（SDF）的体渲染方案，在重建精细几何结构时仍面临挑战。我们揭示其原因在于：1) 体素网格倾向于破坏有助于精细几何学习的颜色-几何依赖性；2) 欠约束的体素网格缺乏空间一致性，且易陷入局部最优解。本文提出Voxurf——一种兼具高效性与精确性的基于体素的表面重建方法。Voxurf通过多项关键设计解决上述问题，包括：1) 两阶段训练流程，依次获取一致的粗粒度形状并恢复精细细节；2) 双路颜色网络以维持颜色-几何依赖性；3) 层级几何特征以促进体素间的信息传播。大量实验表明，Voxurf同时实现了高效率与高质量。在DTU基准测试中，相比以往的完全隐式方法，Voxurf在实现20倍训练加速的同时获得了更高的重建质量。我们的代码已开源在 https://github.com/wutong16/Voxurf。