PointNet has revolutionized how we think about representing point clouds. For classification
and segmentation tasks, the approach and its subsequent variants/extensions are …

PointNet has recently emerged as a popular representation for unstructured point cloud
data, allowing application of deep learning to tasks such as object detection, segmentation …

Point cloud registration is a key problem for robotics and computer vision communities. This
represents estimating a rigid transform which aligns one point cloud to another. Iterative …

Pose graph optimization (PGO) is the problem of estimating a set of poses from pairwise
relative measurements. PGO is a nonconvex problem and, currently, no known technique …

We present novel, convex relaxations for rotation and pose estimation problems that can a
posteriori guarantee global optimality for practical measurement noise levels. Some such …

We introduce a novel method to estimate the essential matrix for two-view Structure from
Motion (SfM). We show that every 3 by 3 essential matrix can be embedded in a 4 by 4 …

High-dimensional geometric patterns appear in many computer vision problems. In this
work, we present high-dimensional convolutional networks for geometric pattern recognition …

Point cloud registration is a crucial problem in computer vision and robotics. Existing
methods either rely on matching local geometric features, which are sensitive to the pose …

The current work introduces a system for fully automatic tracking of native glenohumeral
kinematics in stereo-radiography sequences. The proposed method first applies …

We study the convex hull of SO(n), the set of n*n orthogonal matrices with unit determinant,
from the point of view of semidefinite programming. We show that the convex hull of SO(n) is …