James ElderTrong Thao Tran2024-11-072024-11-072024-09-032024-11-07https://hdl.handle.net/10315/424883D object detection is critical for autonomous driving and traffic analytics. Current research relies on LiDAR-derived ground truth for training and evaluation. However, LiDAR ground truth is expensive and usually inaccurate in the far field due to sparse LiDAR returns. Assuming a fully calibrated camera and a 3D terrain model, we explore whether inexpensive RGB imagery can be used to obtain 3D ground truth based on the bilateral symmetry of motor vehicles. From manually annotated symmetry points and tire-ground contact points, we infer a vertical symmetry plane and 3D point cloud to estimate vehicle location, pose, and dimensions. These estimates are input into a probabilistic model derived from a standard public motor vehicle dataset to form maximum a posteriori estimates of remaining dimensions. Evaluations on a public traffic dataset show that this novel symmetry-based approach is more accurate than LiDAR-based ground-truthing on single frames and comparable to LiDAR-based methods that propagate information across frames.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceElectronic Thesis or Dissertation2024-11-073D vehicle ground truthing3D object detectionautonomous drivingtraffic analyticsLiDAR limitationsRGB imageryvehicle symmetrypose estimationprobabilistic modelingsymmetry-based approachvehicle dimensions3D from a single imagesingle-image 3D reconstruction3D feature learningdataset creation