59 citations · 85 across the 3 of their papers we have counts for
5 papers
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Xingyu Liu +2
6D object pose estimation is a fundamental yet challenging problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliabl…
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
Xingyu Liu, Rico Jonschkowski, Anelia Angelova +1
Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Many existing approaches to this problem require a depth map of the object for b…
MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences
Xingyu Liu, Mengyuan Yan, Jeannette Bohg
Understanding dynamic 3D environment is crucial for robotic agents and many other applications. We propose a novel neural network architecture called for learning repre…
Learning Video Representations from Correspondence Proposals
Xingyu Liu, Joon-Young Lee, Hailin Jin
Correspondences between frames encode rich information about dynamic content in videos. However, it is challenging to effectively capture and learn those due to their irregular str…
FlowNet3D: Learning Scene Flow in 3D Point Clouds
Xingyu Liu, Charles R. Qi, Leonidas J. Guibas
Many applications in robotics and human-computer interaction can benefit from understanding 3D motion of points in a dynamic environment, widely noted as scene flow. While most pre…