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Wei-Chiu Ma

Wei-Chiu Ma
0 BLOG ARTICLES 7 RESEARCH PAPERS
Wei-Chiu Ma is a PhD student at MIT advised by Prof. Antonio Torralba. His research interests lie in the intersection of computer vision and machine learning, in particular low-level vision and 3D vision. He also works part-time at Uber ATG Toronto with Prof. Raquel Urtasun to apply his research to self-driving vehicles.

Research Papers

Convolutional Recurrent Network for Road Boundary Extraction

J. Liang, N. Homayounfar, S. Wang, W.-C. Ma, R. Urtasun
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. [...] [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

Single Image Intrinsic Decomposition Without a Single Intrinsic Image

W. Ma, H, Chu, B. Zhou, R. Urtasun, A. Torralba
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF]
European Conference on Computer Vision (ECCV), 2018

Deep Parametric Continuous Convolutional Neural Networks

S. Wang, S. Suo, W. Ma, A. PokrovskyR. Urtasun
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Hierarchical Recurrent Attention Networks for Structured Online Maps

N. Homayounfar, W. Ma, S. Lakshmikanth, R. Urtasun
In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identifying how many lanes there are and then drawing each one in turn. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Find Your Way by Observing the Sun and Other Semantic Cues

W.-C. Ma, S. Wang, M. Brubaker, S. Fidler, R. Urtasun
In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. [...] [PDF]
International Conference on Robotics and Automation (ICRA), 2017

Forecasting Interactive Dynamics of Pedestrians with Fictitious Play

W. Ma, D. Huang, N. Lee, K. Kitani
We develop predictive models of pedestrian dynamics by encoding the coupled nature of multi-pedestrian interaction using game theory, and deep learning-based visual analysis to estimate person-specific behavior parameters. Building predictive models for multi-pedestrian interactions however, is very challenging due to two reasons [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

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