Get 3D object shape from monocular RGB-D by learn surface and map to frame with DynamicSurf
Get 3D object shape from monocular RGB-D by learn surface and map to frame with DynamicSurf
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid
arXiv paper abstract https://arxiv.org/abs/2311.08159
arXiv PDF paper https://arxiv.org/pdf/2311.08159.pdf
... propose DynamicSurf, a model-free neural implicit surface reconstruction method for high-fidelity 3D modelling of non-rigid surfaces from monocular RGB-D video.
To cope with the lack of multi-view cues in monocular sequences of deforming surfaces ... DynamicSurf exploits depth, surface normals, and RGB losses to improve reconstruction fidelity and optimisation time.
DynamicSurf learns a neural deformation field that maps a canonical representation of the surface geometry to the current frame.
... designing the canonical representation as a learned feature grid which leads to faster and more accurate surface reconstruction than competing approaches that use a single MLP.
... DynamicSurf ... can optimize sequences of varying frames with 6x speedup over pure MLP-based approaches while achieving comparable results to the state-of-the-art methods ...
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