no code implementations • 11 Mar 2020 • Chengyao Li, Jason Ku, Steven L. Waslander
To tackle these two issues, we propose CG-Stereo, a confidence-guided stereo 3D object detection pipeline that uses separate decoders for foreground and background pixels during depth estimation, and leverages the confidence estimation from the depth estimation network as a soft attention mechanism in the 3D object detector.
3D Object Detection From Stereo Images Autonomous Driving +3
no code implementations • 17 Sep 2019 • Alex D. Pon, Jason Ku, Chengyao Li, Steven L. Waslander
The issue with existing stereo matching networks is that they are designed for disparity estimation, not 3D object detection; the shape and accuracy of object point clouds are not the focus.
3D Object Detection From Stereo Images Autonomous Driving +5
no code implementations • 15 Jul 2019 • Jason Ku, Alex D. Pon, Sean Walsh, Steven L. Waslander
Accurately estimating the orientation of pedestrians is an important and challenging task for autonomous driving because this information is essential for tracking and predicting pedestrian behavior.
1 code implementation • CVPR 2019 • Jason Ku, Alex D. Pon, Steven L. Waslander
We present MonoPSR, a monocular 3D object detection method that leverages proposals and shape reconstruction.
Ranked #13 on Vehicle Pose Estimation on KITTI Cars Hard
2 code implementations • 31 Jan 2018 • Jason Ku, Ali Harakeh, Steven L. Waslander
With the rise of data driven deep neural networks as a realization of universal function approximators, most research on computer vision problems has moved away from hand crafted classical image processing algorithms.
4 code implementations • 6 Dec 2017 • Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, Steven Waslander
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.