9 citations · 9 across the 1 of their papers we have counts for
4 papers
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…
Part Segmentation for Highly Accurate Deformable Tracking in Occlusions via Fully Convolutional Neural Networks
Weilin Wan, Aaron Walsman, Dieter Fox
Successfully tracking the human body is an important perceptual challenge for robots that must work around people. Existing methods fall into two broad categories: geometric tracki…
Early Fusion for Goal Directed Robotic Vision
Aaron Walsman, Yonatan Bisk, Saadia Gabriel +4
Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline. M…
Dynamic High Resolution Deformable Articulated Tracking
Aaron Walsman, Weilin Wan, Tanner Schmidt +1
The last several years have seen significant progress in using depth cameras for tracking articulated objects such as human bodies, hands, and robotic manipulators. Most approaches…