Rui Hu
Rui Hu is a research engineer at Uber ATG Toronto. Before that he was a staff engineer at Qualcomm and senior engineer at Apple SPG. His research interests include neural networks, computer vision and GPU optimization.
Research Papers
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
S. Duggal, S. Wang, W.-C. Ma, R. Hu, R. Urtasun
We propose a real-time dense depth estimation approach using stereo image pairs, which utilizes differentiable Patch Match to progressively prune the stereo matching search space. Our model achieves competitive performance on the KITTI benchmark despite running in real time. [PDF]
International Conference on Computer Vision (ICCV), 2019
We propose a real-time dense depth estimation approach using stereo image pairs, which utilizes differentiable Patch Match to progressively prune the stereo matching search space. Our model achieves competitive performance on the KITTI benchmark despite running in real time. [PDF]
International Conference on Computer Vision (ICCV), 2019
Multi-Task Multi-Sensor Fusion for 3D Object Detection
M. Liang, B. Yang, Y. Chen, R. Hu, R. Urtasun
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Deep Rigid Instance Scene Flow
W.-C. Ma, S. Wang, R. Hu, Y. Xiong, R. Urtasun
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
UPSNet: A Unified Panoptic Segmentation Network
Y. Xiong, R. Liao, H. Zhao, R. Hu, M. Bai, E. Yumer, R. Urtasun
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
