We present GO-Surf, a direct feature grid optimization method for accurate
and fast surface reconstruction from RGB-D sequences. We model the underlying
scene with a learned hierarchical feature voxel grid that encapsulates
multi-level geometric and appearance local information. Feature vectors are
directly optimized such that after being tri-linearly interpolated, decoded by
two shallow MLPs into signed distance and radiance values, and rendered via
surface volume rendering, the discrepancy between synthesized and observed
RGB/depth values is minimized. Our supervision signals -- RGB, depth and
approximate SDF -- can be obtained directly from input images without any need
for fusion or post-processing. We formulate a novel SDF gradient regularization
term that encourages surface smoothness and hole filling while maintaining high
frequency details. GO-Surf can optimize sequences of $1$-$2$K frames in
$15$-$45$ minutes, a speedup of $\times60$ over NeuralRGB-D, the most related
approach based on an MLP representation, while maintaining on par performance
on standard benchmarks. Project page: this https URL

GO-Surf 是一种直接的特征网格优化方法，可以从 RGB-D 序列快速准确地重建表面，采用学习的分层特征体素网格对场景建模，以直接优化特征向量并渲染出表面体积，加入 SDF 梯度正则化项以鼓励表面平滑填孔和保持高频细节。其能够在 15 至 45 分钟内优化 1000 至 2000 帧，是现有方法的 60 倍。