Vehicle Pose Estimation: Exploring Angular Representations

Ivan Orlov, Marco Buzzelli, Raimondo Schettini

Abstract: 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 indivi

External IDs:dblp:conf/visigrapp/OrlovBS24