Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …

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 …

We present iNeRF, a framework that performs mesh-free pose estimation by" inverting" a
Neural Radiance Field (NeRF). NeRFs have been shown to be remarkably effective for the …

We introduce an approach for recovering the 6D pose of multiple known objects in a scene
captured by a set of input images with unknown camera viewpoints. First, we present a …

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 …

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 …

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 …

The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances
in an RGB-D image. Contrary to" instance-level" 6D pose estimation tasks, our problem …

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 …