Search Results for author: Jason Ku

Found 6 papers, 3 papers with code

Confidence Guided Stereo 3D Object Detection with Split Depth Estimation

no code implementations11 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

Object-Centric Stereo Matching for 3D Object Detection

no code implementations17 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

Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation

no code implementations15 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.

3D Object Detection Autonomous Driving +2

In Defense of Classical Image Processing: Fast Depth Completion on the CPU

2 code implementations31 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.

Depth Completion

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