Vehicle Pose Estimation: Exploring Angular Representations

This paper addresses the challenge of azimuth estimation in the context of car pose estimation. Our research utilizes the PASCAL3D+ dataset, which offers a diverse range of object categories, including cars, with annotated azimuth estimations for each photograph. We introduce two architectures that approach azimuth estimation as a regression problem, each employing a deep convolutional neural network (DCNN) backbone but diverging in their output definition strategies. The first architecture employs a sin-cos representation of the car’s azimuth, while the second utilizes two directional discriminators, distinguishing between front/rear and left/right views of the vehicle. Our comparative analysis reveals that both architectures demonstrate near-identical performance levels on the PASCAL3D+ validation set, achieving a median error of 3.5◦, which is a significant advancement in the state of the art. The minimal performance disparity between the two methods highlights their individual strengths while also underscoring the similarity in their practical efficacy. This study not only proposes effective solutions for accurate azimuth estimation but also contributes to the broader understanding of pose estimation challenges in automotive contexts. The code is available at https://github.com/vani-or/car_pose_estimation.