In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

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 …

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 …

Current 3D object detection methods are heavily influenced by 2D detectors. In order to
leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids …

A key technical challenge in performing 6D object pose estimation from RGB-D image is to
fully leverage the two complementary data sources. Prior works either extract information …

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 …

We present a novel method for detecting 3D model instances and estimating their 6D poses
from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover …

We propose a single-shot approach for simultaneously detecting an object in an RGB image
and predicting its 6D pose without requiring multiple stages or having to examine multiple …

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 …

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 …