50 citations · 148 across the 10 of their papers we have counts for
10 papers
Predicting Stable Configurations for Semantic Placement of Novel Objects
Chris Paxton, Chris Xie, Tucker Hermans +1
Human environments contain numerous objects configured in a variety of arrangements. Our goal is to enable robots to repose previously unseen objects according to learned semantic…
RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks
Christopher Xie, Arsalan Mousavian, Yu Xiang +1
Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have…
FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling
Christopher Xie, Keunhong Park, Ricardo Martin-Brualla +1
We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images. In contrast to previous work, we are able…
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
William Agnew, Christopher Xie, Aaron Walsman +4
Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly a…
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
Yu Xiang, Christopher Xie, Arsalan Mousavian +1
Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method…
Unseen Object Instance Segmentation for Robotic Environments
Christopher Xie, Yu Xiang, Arsalan Mousavian +1
In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unsee…