3D shape completion for real data is important but challenging, since partial
point clouds acquired by real-world sensors are usually sparse, noisy and
unaligned. Different from previous methods, we address the problem of learning
3D complete shape from unaligned and real-world partial point clouds. To this
end, we propose a weakly-supervised method to estimate both 3D canonical shape
and 6-DoF pose for alignment, given multiple partial observations associated
with the same instance. The network jointly optimizes canonical shapes and
poses with multi-view geometry constraints during training, and can infer the
complete shape given a single partial point cloud. Moreover, learned pose
estimation can facilitate partial point cloud registration. Experiments on both
synthetic and real data show that it is feasible and promising to learn 3D
shape completion through large-scale data without shape and pose supervision.

本研究利用弱监督的方法，通过多视角几何约束来同时估计 3D 的标准形状和 6 自由度姿态，解决了学习未对准和真实世界部分点云的情况下，利用大量数据进行 3D 形状补全的问题。实验证明，学习姿势估计可以促进部分点云的配准。

野外场景弱监督 3D 形状补全

Weakly-supervised 3D Shape Completion in the Wild

The continual improvement of 3D sensors has driven the development of
algorithms to perform point cloud analysis. In fact, techniques for point cloud
classification and segmentation have in recent years achieved incredible
performance driven in part by leveraging large synthetic datasets.
Unfortunately these same state-of-the-art approaches perform poorly when
applied to incomplete point clouds. This limitation of existing algorithms is
particularly concerning since point clouds generated by 3D sensors in the real
world are usually incomplete due to perspective view or occlusion by other
objects. This paper proposes a general model for partial point clouds analysis
wherein the latent feature encoding a complete point clouds is inferred by
applying a local point set voting strategy. In particular, each local point set
constructs a vote that corresponds to a distribution in the latent space, and
the optimal latent feature is the one with the highest probability. This
approach ensures that any subsequent point cloud analysis is robust to partial
observation while simultaneously guaranteeing that the proposed model is able
to output multiple possible results. This paper illustrates that this proposed
method achieves state-of-the-art performance on shape classification, part
segmentation and point cloud completion.

该研究提出了一种针对局部 3D 点云的通用模型，该模型引入了本地点集投票策略，能够解决现有算法在应用于不完整点云时表现不佳的问题，并在形状分类、部分分割和点云补全方面取得了最先进的性能。