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.

本文介绍了一种名为AIR-Nets的注意力隐式表示网络, 用于从点云中重建3D形状。通过利用局部和模块化表示3D形状, AIR-Nets将输入点云编码为一组锚定在3D空间中的局部潜在向量，并具有全局潜在描述，以实现全局一致性。该模型是第一个基于编码器的无棱柿函数的局部描述方法。经验证明，该模型在ShapeNet数据集上显着优于以前的最先进的编码器-隐式形状学习方法，并在稀疏设置中表现优异。此外, 该模型还具有简单运行机制与稀疏潜在表示形式，未来有多种激动人心的研究方向。

AIR-Nets: 一种基于注意力机制的局部条件隐式表示框架