Object pose estimation is a core perception task that enables, for example, object
manipulation and scene understanding. The widely available, inexpensive, and high …

We propose SNI-SLAM a semantic SLAM system utilizing neural implicit representation that
simultaneously performs accurate semantic mapping high-quality surface reconstruction and …

For robots to be useful outside labs and specialized factories we need a way to teach them
new useful behaviors quickly. Current approaches lack either the generality to onboard new …

Recent advances in neural radiance fields enable novel view synthesis of photo-realistic
images in dynamic settings, which can be applied to scenarios with human animation …

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 …

Robotic manipulation can greatly benefit from the data efficiency, robustness, and
predictability of model-based methods if robots can quickly generate models of novel objects …

Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

Object pose refinement is essential for robust object pose estimation. Previous work has
made significant progress towards instance-level object pose refinement. Yet category-level …

Category-level pose estimation is a challenging task with many potential applications in
computer vision and robotics. Recently, deep-learning-based approaches have made great …

We address the problem of keypoint selection, and find that the performance of 6DoF pose
estimation methods can be improved when pre-defined keypoint locations are learned …