We 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, one of the most challenging settings for 3D reconstruction,
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. We depart from current neural non-rigid surface
reconstruction models by 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. We demonstrate DynamicSurf on
public datasets and show that it can optimize sequences of varying frames with
$6\times$ speedup over pure MLP-based approaches while achieving comparable
results to the state-of-the-art methods. Project is available at
this https URL

通过利用深度、表面法线和 RGB 损失来改善重建保真度和优化时间的缺乏多视图线索的非刚性表面的高保真度三维建模的无模型神经隐式表面重建方法 DynamicSurf。