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: this https URL

通过使用截断有符号距离场（TSDF）的方法，本研究提出了一种能够减少采样数量的新方法，有效提高了多视图神经表面重建的推断速度，同时保持了渲染质量。该方法在各种神经表面场模型中都能被可靠地插入使用，并在性能方面表现出色。