The 6D pose estimation of an object from an image is a central problem in many domains of
Computer Vision (CV) and researchers have struggled with this issue for several years …

Interest point detection and local feature description are fundamental steps in many
computer vision applications. Classical approaches are based on a detect-then-describe …

The advances in monocular 3D human pose estimation are dominated by supervised
techniques that require large-scale 2D/3D pose annotations. Such methods often behave …

Object pose estimation is an important component of most vision pipelines for embodied
agents, as well as in 3D vision more generally. In this paper we tackle the problem of …

We present a large-scale stereo RGB image object pose estimation dataset named the
StereOBJ-1M dataset. The dataset is designed to address challenging cases such as object …

Accurately describing and detecting 2D and 3D keypoints is crucial to establishing
correspondences across images and point clouds. Despite a plethora of learning-based 2D …

Existing approaches for learning local image descriptors have shown remarkable
achievements in a wide range of geometric tasks. However, most of them require per-pixel …

Event cameras have emerged as a promising vision sensor in recent years due to their
unparalleled temporal resolution and dynamic range. While registration of 2D RGB images …

6D pose estimation of textureless objects is a valuable but challenging task for many robotic
applications. In this work, we propose a framework to address this challenge using only RGB …

We present a deployment friendly, fast bottom-up framework for multi-person 3D human
pose estimation. We adopt a novel neural representation of multi-person 3D pose which …