Indoor scene reconstruction from monocular images has long been sought after
by augmented reality and robotics developers. Recent advances in neural field
representations and monocular priors have led to remarkable results in
scene-level surface reconstructions. The reliance on Multilayer Perceptrons
(MLP), however, significantly limits speed in training and rendering. In this
work, we propose to directly use signed distance function (SDF) in sparse voxel
block grids for fast and accurate scene reconstruction without MLPs. Our
globally sparse and locally dense data structure exploits surfaces' spatial
sparsity, enables cache-friendly queries, and allows direct extensions to
multi-modal data such as color and semantic labels. To apply this
representation to monocular scene reconstruction, we develop a scale
calibration algorithm for fast geometric initialization from monocular depth
priors. We apply differentiable volume rendering from this initialization to
refine details with fast convergence. We also introduce efficient
high-dimensional Continuous Random Fields (CRFs) to further exploit the
semantic-geometry consistency between scene objects. Experiments show that our
approach is 10x faster in training and 100x faster in rendering while achieving
comparable accuracy to state-of-the-art neural implicit methods.

研究使用 “符号距离函数” 结合 “稀疏体素块网格” 等算法实现室内场景的平面重建，相较于常规算法在训练与渲染速度上有数量级的提升，同时保持与现有方法相当的精度。