  In recent years, there is an influx of deep learning models for 3D vehicle
object detection. However, little attention was paid to orientation prediction.
Existing research work proposed various vehicle orientation representation
methods for deep learning, however a holistic, systematic review has not been
conducted. Through our experiments, we categorize and compare the accuracy
performance of various existing orientation representations using the KITTI 3D
object 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 (positional
encoding and depth map) not boosting prediction performance. Our code is
published on GitHub:
https://github.com/umd-fire-coml/KITTI-orientation-learning
