Object pose detection and tracking has recently attracted increasing attention due to its wide
applications in many areas, such as autonomous driving, robotics, and augmented reality …

Abstract 6D pose estimation from a single RGB image is a fundamental task in computer
vision. The current top-performing deep learning-based methods rely on an indirect strategy …

6D pose estimation is a common and important task in industry. Obtaining the 6D pose of
objects is the basis for many other functions such as bin picking, autopilot, etc. Therefore …

In this work, we present FFB6D, a full flow bidirectional fusion network designed for 6D pose
estimation from a single RGBD image. Our key insight is that appearance information in the …

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 …

While 6D object pose estimation has recently made a huge leap forward, most methods can
still only handle a single or a handful of different objects, which limits their applications. To …

This paper addresses the challenge of 6DoF pose estimation from a single RGB image
under severe occlusion or truncation. Many recent works have shown that a two-stage …

In this work, we present a novel data-driven method for robust 6DoF object pose estimation
from a single RGBD image. Unlike previous methods that directly regressing pose …

Using synthetic data for training deep neural networks for robotic manipulation holds the
promise of an almost unlimited amount of pre-labeled training data, generated safely out of …

Estimating the 6D pose of known objects is important for robots to interact with the real
world. The problem is challenging due to the variety of objects as well as the complexity of a …