We present ANISE, a method that reconstructs a 3D~shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. The shape is formulated as an assembly of neural implicit functions, each representing a different part instance. In contrast to previous approaches, the prediction of this representation proceeds in a coarse-to-fine manner. Our model first reconstructs a structural arrangement of the shape in the form of geometric transformations of its part instances. Conditioned on them, the model predicts part latent codes encoding their surface geometry. Reconstructions can be obtained in two ways: (i) by directly decoding the part latent codes to part implicit functions, then combining them into the final shape; or (ii) by using part latents to retrieve similar part instances in a part database and assembling them in a single shape. We demonstrate that, when performing reconstruction by decoding part representations into implicit functions, our method achieves state-of-the-art part-aware reconstruction results from both images and sparse point clouds.When reconstructing shapes by assembling parts retrieved from a dataset, our approach significantly outperforms traditional shape retrieval methods even when significantly restricting the database size. We present our results in well-known sparse point cloud reconstruction and single-view reconstruction benchmarks.

翻译：我们提出ANISE方法，该方法利用部件感知的神经隐式形状表示，从部分观测（图像或稀疏点云）中重建三维形状。该形状被表述为多个神经隐式函数的组装，每个函数对应不同的部件实例。与以往方法不同，该表示的预测采用由粗到精的方式。我们的模型首先以部件实例的几何变换形式重建形状的结构布局。在此基础上，模型预测用于编码部件表面几何的部件隐向量。重建可通过两种方式获得：(i) 直接将部件隐向量解码为部件隐式函数，再组合成最终形状；或(ii) 利用部件隐向量从部件数据库中检索相似部件实例，并将其组装为单一形状。我们证明，在通过将部件表示解码为隐式函数进行重建时，该方法在图像和稀疏点云的重建结果上均达到了最先进的部件感知重建性能。当通过组装数据集中检索到的部件来重建形状时，即使大幅限制数据库规模，我们的方法仍显著优于传统形状检索方法。我们在著名的稀疏点云重建和单视图重建基准上展示了实验结果。