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 …

This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a
hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D …

We present 3DP3, a framework for inverse graphics that uses inference in a structured
generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent …

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 …

We propose a view-based approach for labeling objects in 3D scenes reconstructed from
RGB-D (color+ depth) videos. We utilize sliding window detectors trained from object views …

In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can
naturally handle pose-related uncertainties and ambiguities arising in almost all real life …

Placing is a necessary skill for a personal robot to have in order to perform tasks such as
arranging objects in a disorganized room. The object placements should not only be stable …

Reasoning about uncertain orientations is one of the core problems in many perception
tasks such as object pose estimation or motion estimation. In these scenarios, poor …

A number of pattern recognition tasks,\textit {eg}, face verification, can be boiled down to
classification or clustering of unit length directional feature vectors whose distance can be …