This work addresses the problem of \textit{shape completion}, i.e., the task of restoring incomplete shapes by predicting their missing parts. While previous works have often predicted the fractured and restored shape in one step, we approach the task by separately predicting the fractured and newly restored parts, but ensuring these predictions are interconnected. We use a decoder network motivated by related work on the prediction of signed distance functions (DeepSDF). In particular, our representation allows us to consider test-time-training, i.e., finetuning network parameters to match the given incomplete shape more accurately during inference. While previous works often have difficulties with artifacts around the fracture boundary, we demonstrate that our overfitting to the fractured parts leads to significant improvements in the restoration of eight different shape categories of the ShapeNet data set in terms of their chamfer distances.

本研究解决了形状补全的问题，即通过预测缺失部分来恢复不完整的形状。我们提出了一种新方法，将预测的破裂部分与新恢复部分分开，同时确保两者之间的联系。我们发现该方法在八种不同形状类别的补全效果显著优于以往研究，尤其是在减少破裂边界附近的伪影方面。

测试时训练的3D形状补全