多视角点云配准：基于自编码器潜在空间的优化方法

Multiview Point Cloud Registration via Optimization in an Autoencoder Latent Space

April 30, 2025

作者: Luc Vedrenne, Sylvain Faisan, Denis Fortun

cs.AI

摘要

点云刚性配准是三维计算机视觉中的一个基础问题。在多视角场景下，我们的目标是找到一组6D位姿以对齐多个物体。基于成对配准的方法依赖于后续的同步算法，这使得它们在视图数量增加时扩展性较差。生成式方法克服了这一限制，但基于高斯混合模型并使用期望最大化算法，因此不太适合处理大范围的变换。此外，大多数现有方法无法应对高度退化的情况。本文中，我们提出了POLAR（POint cloud LAtent Registration，点云潜在配准），这是一种多视角配准方法，能够高效处理大量视图，同时对高度退化和大初始角度具有鲁棒性。为此，我们将配准问题转换到预训练自编码器的潜在空间中，设计了一种考虑退化的损失函数，并开发了一种高效的多起点优化策略。我们提出的方法在合成数据和真实数据上均显著优于现有最先进方法。POLAR可在github.com/pypolar/polar获取，或作为独立包通过pip install polaregistration安装。

English

Point cloud rigid registration is a fundamental problem in 3D computer vision. In the multiview case, we aim to find a set of 6D poses to align a set of objects. Methods based on pairwise registration rely on a subsequent synchronization algorithm, which makes them poorly scalable with the number of views. Generative approaches overcome this limitation, but are based on Gaussian Mixture Models and use an Expectation-Maximization algorithm. Hence, they are not well suited to handle large transformations. Moreover, most existing methods cannot handle high levels of degradations. In this paper, we introduce POLAR (POint cloud LAtent Registration), a multiview registration method able to efficiently deal with a large number of views, while being robust to a high level of degradations and large initial angles. To achieve this, we transpose the registration problem into the latent space of a pretrained autoencoder, design a loss taking degradations into account, and develop an efficient multistart optimization strategy. Our proposed method significantly outperforms state-of-the-art approaches on synthetic and real data. POLAR is available at github.com/pypolar/polar or as a standalone package which can be installed with pip install polaregistration.