3D textured shape recovery from partial scans is crucial for many real-world applications. Existing approaches have demonstrated the efficacy of implicit function representation, but they suffer from partial inputs with severe occlusions and varying object types, which greatly hinders their application value in the real world. This technical report presents our approach to address these limitations by incorporating learned geometric priors. To this end, we generate a SMPL model from learned pose prediction and fuse it into the partial input to add prior knowledge of human bodies. We also propose a novel completeness-aware bounding box adaptation for handling different levels of scales and partialness of partial scans.
翻译:3D 纹理形状从部分扫描中恢复对于许多现实世界应用至关重要。 现有的方法已经证明了隐含功能代表的功效,但是它们受到了部分投入的损害,这些投入具有严重的分层和不同种类的物体,严重妨碍了它们在现实世界中的应用价值。本技术报告介绍了我们通过纳入所学的几何前科来克服这些限制的方法。为此,我们从学到的SMPL模型中产生一个SMPL 模型,从中生成一个SMPL 模型,将其结合到部分投入中,以增加人类身体先前的知识。我们还提议采用新的完整度约束箱,处理不同程度的规模和部分扫描的片面性。