As real-scanned point clouds are mostly partial due to occlusions and
viewpoints, reconstructing complete 3D shapes based on incomplete observations
becomes a fundamental problem for computer vision. With a single incomplete
point cloud, it becomes the partial point cloud completion problem. Given
multiple different observations, 3D reconstruction can be addressed by
performing partial-to-partial point cloud registration. Recently, a large-scale
Multi-View Partial (MVP) point cloud dataset has been released, which consists
of over 100,000 high-quality virtual-scanned partial point clouds. Based on the
MVP dataset, this paper reports methods and results in the Multi-View Partial
Point Cloud Challenge 2021 on Completion and Registration. In total, 128
participants registered for the competition, and 31 teams made valid
submissions. The top-ranked solutions will be analyzed, and then we will
discuss future research directions.

该研究报告探讨利用 MVP 数据集解决部分点云完成和注册的问题，介绍 2021 年的挑战赛，讨论未来方向。

2021 年多视图部分点云完成和配准挑战：方法与结果

Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and  Registration: Methods and Results

Point cloud registration is a key task in many computational fields. Previous
correspondence matching based methods require the inputs to have distinctive
geometric structures to fit a 3D rigid transformation according to point-wise
sparse feature matches. However, the accuracy of transformation heavily relies
on the quality of extracted features, which are prone to errors with respect to
partiality and noise. In addition, they can not utilize the geometric knowledge
of all the overlapping regions. On the other hand, previous global feature
based approaches can utilize the entire point cloud for the registration,
however they ignore the negative effect of non-overlapping points when
aggregating global features. In this paper, we present OMNet, a global feature
based iterative network for partial-to-partial point cloud registration. We
learn overlapping masks to reject non-overlapping regions, which converts the
partial-to-partial registration to the registration of the same shape.
Moreover, the previously used data is sampled only once from the CAD models for
each object, resulting in the same point clouds for the source and reference.
We propose a more practical manner of data generation where a CAD model is
sampled twice for the source and reference, avoiding the previously prevalent
over-fitting issue. Experimental results show that our method achieves
state-of-the-art performance compared to traditional and deep learning based
methods. Code is available at this https URL

本研究提出了一种基于全局特征的 OMNet 方法，用于局部 - 局部点云配准。通过学习重叠区域的掩码来拒绝不重叠的区域，并采用了更实用的数据生成方法，实现了与传统和深度学习方法相比的最新性能。

OMNet：用于部分到部分点云配准的重叠遮罩学习

OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud  Registration

While 3D-3D registration is traditionally tacked by optimization-based
methods, recent work has shown that learning-based techniques could achieve
faster and more robust results. In this context, however, only PRNet can handle
the partial-to-partial registration scenario. Unfortunately, this is achieved
at the cost of relying on an iterative procedure, with a complex network
architecture. Here, we show that learning-based partial-to-partial registration
can be achieved in a one-shot manner, jointly reducing network complexity and
increasing registration accuracy. To this end, we propose an Optimal Transport
layer able to account for occluded points thanks to the use of outlier bins.
The resulting OPRNet framework outperforms the state of the art on standard
benchmarks, demonstrating better robustness and generalization ability than
existing techniques.

本文提出了一种基于 Optimal Transport layer 和 outlier bins 的 OPRNet 框架，能够以一次迭代的方式实现学习型 partial-to-partial registration，这可以在降低网络复杂度的同时显著提高配准精度