Human beings acquire knowledge by a continually learning process. They learn through
experience, accumulate knowledge, and employ it to perform the task at hand. The main aim …

Local processing is an essential feature of CNNs and other neural network architectures--it
is one of the reasons why they work so well on images where relevant information is, to a …

In this paper, we propose a novel real-time 6D object pose estimation framework, named
G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and …

The challenges of learning a robust 6D pose function lie in 1) severe occlusion and 2)
systematic noises in depth images. Inspired by the success of point-pair features, the goal of …

Predicting the object's 6D pose from a single RGB image is a fundamental computer vision
task. Generally, the distance between transformed object vertices is employed as an …

Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often
adopt a twostage pipeline by establishing correspondences and utilizing a RANSAC …

Monocular object 6D pose estimation is a fundamental yet challenging task in computer
vision. Recently, deep learning has been proven to be capable of predicting remarkable …

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 …

Establishment of point correspondence between camera and object coordinate systems is a
promising way to solve 6D object poses. However, surrogate objectives of correspondence …

Abstract 6D pose estimation has garnered significant attention and research. RGB images
and point clouds converted from RGB-D images provide complementary color and geometry …