We present a method that can recognize new objects and estimate their 3D pose in RGB
images even under partial occlusions. Our method requires neither a training phase on …

In this paper, we introduce neural texture learning for 6D object pose estimation from
synthetic data and a few unlabelled real images. Our major contribution is a novel learning …

Compared to 2D object bounding-box labeling, it is very difficult for humans to annotate 3D
object poses, especially when depth images of scenes are unavailable. This paper …

We propose a novel keypoint voting scheme based on intersecting spheres, that is more
accurate than existing schemes and allows for fewer, more disperse keypoints. The scheme …

We introduce the concept of geometric stability to the problem of 6D object pose estimation
and propose to learn pose inference based on geometrically stable patches extracted from …

Estimating the relative pose of a new object without prior knowledge is a hard problem,
while it is an ability very much needed in robotics and Augmented Reality. We present a …

Object pose estimation is important for object manipulation and scene understanding. In
order to improve the general applicability of pose estimators, recent research focuses on …

This paper addresses the problem of learning to estimate the depth of detected objects
given some measurement of camera motion (eg, from robot kinematics or vehicle odometry) …

Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

We address the problem of keypoint selection, and find that the performance of 6DoF pose
estimation methods can be improved when pre-defined keypoint locations are learned …