TSDF-Sampling: Efficient Sampling for Neural Surface Field using
  Truncated Signed Distance Field
Chaerin Min Sehyun Cha Changhee Won Jongwoo Lim
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/