Registration is a transformation estimation problem between two point clouds, which has a
unique and critical role in numerous computer vision applications. The developments of …

Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult …

We introduce OpenShape, a method for learning multi-modal joint representations of text,
image, and point clouds. We adopt the commonly used multi-modal contrastive learning …

We introduce PREDATOR, a model for pairwise pointcloud registration with deep attention
to the overlap region. Different from previous work, our model is specifically designed to …

Arguably one of the top success stories of deep learning is transfer learning. The finding that
pre-training a network on a rich source set (eg, ImageNet) can help boost performance once …

We study the problem of extracting correspondences between a pair of point clouds for
registration. For correspondence retrieval, existing works benefit from matching sparse …

The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted
descriptors. However, it is widely despised by recent deep matchers that obtain the rotation …

Removing outlier correspondences is one of the critical steps for successful feature-based
point cloud registration. Despite the increasing popularity of introducing deep learning …

Abstract We present Deep Global Registration, a differentiable framework for pairwise
registration of real-world 3D scans. Deep global registration is based on three modules: a 6 …