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

Establishing correspondences from image to 3D has been a key task of 6DoF object pose
estimation for a long time. To predict pose more accurately, deeply learned dense maps …

Estimating the 6D object pose from a single RGB image often involves noise and
indeterminacy due to challenges such as occlusions and cluttered backgrounds. Meanwhile …

Category-level object pose estimation aiming to predict the 6D pose and 3D size of objects
from known categories typically struggles with large intra-class shape variation. Existing …

The ability to successfully grasp objects is crucial in robotics, as it enables several
interactive downstream applications. To this end, most approaches either compute the full …

Object pose estimation is an integral part of robot vision and AR. Previous 6D pose retrieval
pipelines treat the problem either as a regression task or discretize the pose space to …

We present an entirely new geometric and probabilistic approach to synchronization of
correspondences across multiple sets of objects or images. In particular, we present two …

Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile
devices due to its small size and affordable price. Previous works have mainly focused on …

Time-varying, smooth trajectory estimation is of great interest to the vision community for
accurate 3D systems. In this paper, we propose a novel principal component local …

3D Point clouds are a rich source of information that enjoy growing popularity in the vision
community. However, due to the sparsity of their representation, learning models based on …