Multi-view neural surface reconstruction has exhibited impressive results. However, a notable limitation is the prohibitively slow inference time when compared to traditional techniques, primarily attributed to the dense sampling, required to maintain the rendering quality. This paper introduces a novel approach that substantially reduces the number of samplings by incorporating the Truncated Signed Distance Field (TSDF) of the scene. While prior works have proposed importance sampling, their dependence on initial uniform samples over the entire space makes them unable to avoid performance degradation when trying to use less number of samples. In contrast, our method leverages the TSDF volume generated only by the trained views, and it proves to provide a reasonable bound on the sampling from upcoming novel views. As a result, we achieve high rendering quality by fully exploiting the continuous neural SDF estimation within the bounds given by the TSDF volume. Notably, our method is the first approach that can be robustly plug-and-play into a diverse array of neural surface field models, as long as they use the volume rendering technique. Our empirical results show an 11-fold increase in inference speed without compromising performance. The result videos are available at our project page: https://tsdf-sampling.github.io/

翻译：多视角神经曲面重建展现出了令人瞩目的成果。然而，一个显著的局限在于其推理速度远慢于传统技术，这主要归因于为了保持渲染质量而必须进行的密集采样。本文提出了一种新颖方法，通过引入场景的截断符号距离场（TSDF）大幅减少了采样次数。虽然先前的研究提出了重要性采样，但这些方法依赖于对整个空间的初始均匀采样，因此在试图减少采样数量时无法避免性能下降。相比之下，我们的方法仅利用已训练视图生成的TSDF体积，并证明其能为后续新视角的采样提供合理边界。由此，我们通过在TSDF体积给定的边界内充分利用连续的神经SDF估计，实现了高渲染质量。值得注意的是，本方法是首个能够稳健地即插即用到多种神经曲面场模型（只要它们采用体渲染技术）的方案。实证结果表明，我们在不牺牲性能的情况下将推理速度提升了11倍。结果视频可见于项目主页：https://tsdf-sampling.github.io/