We present the HANDAL dataset for category-level object pose estimation ...
Test-time adaptation methods have been gaining attention recently as a
p...
We present a near real-time method for 6-DoF tracking of an unknown obje...
We introduce MegaPose, a method to estimate the 6D pose of novel objects...
We present a unified and compact representation for object rendering, 3D...
We present a parallelized optimization method based on fast Neural Radia...
Task planning can require defining myriad domain knowledge about the wor...
Neural approximations of scalar and vector fields, such as signed distan...
We propose a single-stage, category-level 6-DoF pose estimation algorith...
We present a large-scale synthetic dataset for novel view synthesis
cons...
We present a new dataset for 6-DoF pose estimation of known objects, wit...
Rendering articulated objects while controlling their poses is critical ...
Unsupervised generation of high-quality multi-view-consistent images and...
Prior work on 6-DoF object pose estimation has largely focused on
instan...
We present a Python-based renderer built on NVIDIA's OptiX ray tracing e...
We introduce DexYCB, a new dataset for capturing hand grasping of object...
We present a system for multi-level scene awareness for robotic manipula...
We present a visually grounded hierarchical planning algorithm for
long-...
Deep learning-based object pose estimators are often unreliable and
over...
The dominant way to control a robot manipulator uses hand-crafted
differ...
We present a robotic grasping system that uses a single external monocul...
Traditional robotic approaches rely on an accurate model of the environm...
In comparison with person re-identification (ReID), which has been widel...
Using simulation to train robot manipulation policies holds the promise ...
We present an approach for estimating the pose of a camera with respect ...
We present a deep reinforcement learning approach to grasp semantically
...
Viewpoint estimation for known categories of objects has been improved
s...
Using synthetic data for training deep neural networks for robotic
manip...
We present a system to infer and execute a human-readable program from a...
We present a new dataset, called Falling Things (FAT), for advancing the...
We present a system for training deep neural networks for object detecti...