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In recent years, there is an influx of deep learning models for 3D vehicleobject detection. However, little attention was paid to orientation prediction.Existing research work proposed various vehicle orientation representationmethods for deep learning, however a holistic, systematic review has not beenconducted. Through our experiments, we categorize and compare the accuracyperformance of various existing orientation representations using the KITTI 3Dobject detection dataset, and propose a new form of orientation representation:Tricosine. Among these, the 2D Cartesian-based representation, or Single Bin,achieves the highest accuracy, with additional channeled inputs (positionalencoding and depth map) not boosting prediction performance. Our code ispublished on GitHub:https://github.com/umd-fire-coml/KITTI-orientation-learning

SoK: Vehicle Orientation Representations for Deep Rotation Estimation