The challenge of transforming partial point clouds into complete meshes still persists, with current methods facing issues like data accessibility constraint, shape preservation failure and poor robustness on real-scan data. Drawing inspiration from the structural information of objects to enhance the completion, we introduce an innovative weakly-supervised shape completion method leveraging structural decomposition without the necessity of SDFs during training. By representing objects as abstract structural frameworks and part details, our method initiates by forecasting the structure of the input partial point clouds, and individually restore each component through part decomposition completion and generation. Extracted part details are represented in images, which are porous and incomplete. Hence, we utilize a completion network to complete such details. For multiple results generation, a diffusion-based generation network is employed to generate a variety of details for the missing areas. The predicted structure and details are subsequently converted back into meshes, yielding the complete results. Since the details are depicted in images, our approach eliminates the need for SDFs during the training phase, achieving weakly-supervision. We conduct extensive comparisons on both artificial and real-scan datasets, demonstrating an average improvement of over 38.1% compared to the prior method, and achieving SOTA performance.

Structural representation is crucial for reconstructing and generating editable 3D shapes with part semantics. Recent 3D shape generation works employ complicated networks and structure definitions relying on hierarchical annotations and pay less attention to the details inside parts. In this paper, we propose the method that parameterizes the shared structure in the same category using a differentiable template and corresponding fixed-length parameters. Specific parameters are fed into the template to calculate cuboids that indicate a concrete shape. We utilize the boundaries of three-view renderings of each cuboid to further describe the inside details. Shapes are represented with the parameters and three-view details inside cuboids, from which the SDF can be calculated to recover the object. Benefiting from our fixed-length parameters and three-view details, our networks for reconstruction and generation are simple and effective to learn the latent space. Our method can reconstruct or generate diverse shapes with complicated details, and interpolate them smoothly. Extensive evaluations demonstrate the superiority of our method on reconstruction from point cloud, generation, and interpolation.

Unsupervised completion of real scene objects is of vital importance but still remains extremely challenging in preserving input shapes, predicting accurate results, and adapting to multi-category data. To solve these problems, we propose in this paper an Unsupervised Symmetric Shape-Preserving Autoencoding Network, termed USSPA, to predict complete point clouds of objects from real scenes. One of our main observations is that many natural and manmade objects exhibit significant symmetries. To accommodate this, we devise a symmetry learning module to learn from those objects and to preserve structural symmetries. Starting from an initial coarse predictor, our autoencoder refines the complete shape with a carefully designed upsampling refinement module. Besides the discriminative process on the latent space, the discriminators of our USSPA also take predicted point clouds as direct guidance, enabling more detailed shape prediction. Clearly different from previous methods which train each category separately, our USSPA can be adapted to the training of multi-category data in one pass through a classifier-guided discriminator, with consistent performance on single category. For more accurate evaluation, we contribute to the community a real scene dataset with paired CAD models as ground truth. Extensive experiments and comparisons demonstrate our superiority and generalization and show that our method achieves state-of-the-art performance on unsupervised completion of real scene objects.