Ming Liang
Ming Liang is a research scientist at Uber ATG Toronto. Before that he was a senior engineer at Apple SPG. His research interests include neural networks and computer vision.
Research Papers
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
HDNET: Exploiting HD Maps for 3D Object Detection
B. Yang, M. Liang, R. Urtasun
In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design a single stage detector that extracts geometric and semantic features from the HD maps. [...] [PDF]
Conference on Robot Learning (CORL), 2018
In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design a single stage detector that extracts geometric and semantic features from the HD maps. [...] [PDF]
Conference on Robot Learning (CORL), 2018
Deep Continuous Fusion for Multi-Sensor 3D Object Detection
M. Liang, B. Yang, S. Wang, R. Urtasun
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. [...] [PDF]
European Conference on Computer Vision (ECCV), 2018
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. [...] [PDF]
European Conference on Computer Vision (ECCV), 2018
