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3D textured shape recovery from partial scans is crucial for many real-worldapplications. Existing approaches have demonstrated the efficacy of implicitfunction representation, but they suffer from partial inputs with severeocclusions and varying object types, which greatly hinders their applicationvalue in the real world. This technical report presents our approach to addressthese limitations by incorporating learned geometric priors. To this end, wegenerate a SMPL model from learned pose prediction and fuse it into the partialinput to add prior knowledge of human bodies. We also propose a novelcompleteness-aware bounding box adaptation for handling different levels ofscales and partialness of partial scans.

3D Textured Shape Recovery with Learned Geometric Priors