Abstract 3D object detection received increasing attention in autonomous driving recently.
Objects in 3D scenes are distributed with diverse orientations. Ordinary detectors do not …

In this paper, we present a generalizable model-free 6-DoF object pose estimator called
Gen6D. Existing generalizable pose estimators either need the high-quality object models or …

Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D
poses in cluttered scenes presenting significant challenges for model generalizability …

It is difficult to precisely annotate object instances and their semantics in 3D space, and as
such, synthetic data are extensively used for these tasks, eg, category-level 6D object pose …

Rotation estimation of high precision from an RGB-D object observation is a huge challenge
in 6D object pose estimation, due to the difficulty of learning in the non-linear space of SO …

Despite the significant progress in six degrees-of-freedom (6DoF) object pose estimation
existing methods have limited applicability in real-world scenarios involving embodied …

Category-level object pose estimation aiming to predict the 6D pose and 3D size of objects
from known categories typically struggles with large intra-class shape variation. Existing …

We propose a new score-based approach to generate 3D molecules represented as atomic
densities on regular grids. First, we train a denoising neural network that learns to map from …

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

The accurate estimation of six degrees-of-freedom (6DoF) object poses is essential for many
applications in robotics and augmented reality. However, existing methods for 6DoF pose …