135 citations · 192 across the 5 of their papers we have counts for
6 papers
Confidence Guided Stereo 3D Object Detection with Split Depth Estimation
Chengyao Li, Jason Ku, Steven L. Waslander
Accurate and reliable 3D object detection is vital to safe autonomous driving. Despite recent developments, the performance gap between stereo-based methods and LiDAR-based methods…
Object-Centric Stereo Matching for 3D Object Detection
Alex D. Pon, Jason Ku, Chengyao Li +1
Safe autonomous driving requires reliable 3D object detection-determining the 6 DoF pose and dimensions of objects of interest. Using stereo cameras to solve this task is a cost-ef…
Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation
Jason Ku, Alex D. Pon, Sean Walsh +1
Accurately estimating the orientation of pedestrians is an important and challenging task for autonomous driving because this information is essential for tracking and predicting p…
Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction
Jason Ku, Alex D. Pon, Steven L. Waslander
We present MonoPSR, a monocular 3D object detection method that leverages proposals and shape reconstruction. First, using the fundamental relations of a pinhole camera model, dete…
In Defense of Classical Image Processing: Fast Depth Completion on the CPU
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 c…
Joint 3D Proposal Generation and Object Detection from View Aggregation
Jason Ku, Melissa Mozifian, Jungwook Lee +2
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to gen…