We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region. Different from previous work, our model is specifically designed to handle (also) point-cloud pairs with low overlap. Its key novelty is an overlap-attention block for early information exchange between the latent encodings of the two point clouds. In this way the subsequent decoding of the latent representations into per-point features is conditioned on the respective other point cloud, and thus can predict which points are not only salient, but also lie in the overlap region between the two point clouds. The ability to focus on points that are relevant for matching greatly improves performance: PREDATOR raises the rate of successful registrations by more than 20% in the low-overlap scenario, and also sets a new state of the art for the 3DMatch benchmark with 89% registration recall. Source code and pre-trained models will be available at https://github.com/ShengyuH/OverlapPredator.

我们引入了PREDATOR模型，这是一种针对点云配对注册的模型，通过深度注意重叠区域来实现，其重要特点是重叠注意力块用于两个点云的潜在编码之间的早期信息交换，从而可以预测哪些点不仅很重要，而且位于两个点云之间的重叠区域，大大提高了匹配的准确性，这项工作在低重叠情况下成功匹配率提高了20％以上，在3DMatch benchmark上也创造了新的纪录，注册召回率为89％。

PREDATOR: 低重叠3D点云注册