Indoor 3D scene reconstruction 10x faster learn and 100x faster render using sparse voxels with Dong
Indoor 3D scene reconstruction 10x faster learn and 100x faster render using sparse voxels with Dong
Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids
arXiv paper abstract https://arxiv.org/abs/2305.13220
arXiv PDF paper https://arxiv.org/pdf/2305.13220.pdf
Indoor scene reconstruction from monocular images … advances in neural field representations and monocular priors have led to remarkable results in scene-level surface reconstructions.
… propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction
… 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.
… develop a scale calibration algorithm for fast geometric initialization from monocular depth priors … apply differentiable volume rendering from this initialization to refine details with fast convergence.
… introduce efficient high-dimensional Continuous Random Fields (CRFs) to further exploit the semantic-geometry consistency between scene objects.
… approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.
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