This paper introduces Attentive Implicit Representation Networks (AIR-Nets), a simple, but highly effective architecture for 3D reconstruction from point clouds. Since representing 3D shapes in a local and modular fashion increases generalization and reconstruction quality, AIR-Nets encode an input point cloud into a set of local latent vectors anchored in 3D space, which locally describe the object's geometry, as well as a global latent description, enforcing global consistency. Our model is the first grid-free, encoder-based approach that locally describes an implicit function. The vector attention mechanism from [Zhao et al. 2020] serves as main point cloud processing module, and allows for permutation invariance and translation equivariance. When queried with a 3D coordinate, our decoder gathers information from the global and nearby local latent vectors in order to predict an occupancy value. Experiments on the ShapeNet dataset show that AIR-Nets significantly outperform previous state-of-the-art encoder-based, implicit shape learning methods and especially dominate in the sparse setting. Furthermore, our model generalizes well to the FAUST dataset in a zero-shot setting. Finally, since AIR-Nets use a sparse latent representation and follow a simple operating scheme, the model offers several exiting avenues for future work. Our code is available at https://github.com/SimonGiebenhain/AIR-Nets.
翻译:本文介绍了从点云中进行3D重建的简单但非常有效的结构“强化隐含代表网络 ” (AIR-Nets),这是一个简单而高效的3D结构,它代表了本地和模块化时的3D形状,提高了一般化和重建质量,因此AIR-Nets将一个输入点云编码成一组基于3D空间的本地潜在矢量,以局部描述天体的几何以及全球潜伏描述,以强制实施全球一致性。我们的模型是第一个无网格的、基于编码器的本地描述隐含功能的方法。[Zhao等人.2020] 的矢量关注机制作为主点云处理模块,允许变异和翻译变异性。在3D协调下询问时,我们的解码器从全球和附近的本地潜伏矢量矢量矢量矢量中收集信息,以预测占用值。 ShapeNet的实验显示, AIR-Net 明显超越了先前的状态、基于coder的、隐含的构建学习方法,特别是在稀释的设置中。此外,我们的模型将A-G-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-O-S-S-S-S-S-S-S-S-O-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-O-S-S-S-S-S-S-S-S-S-S-S-Sir-O-S-S-S-O-O-O-S-S-S-S-S-O-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-O-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-