SoK: Vehicle Orientation Representations for Deep Rotation Estimation

• URL: http://arxiv.org/abs/2112.04421v1
• Date: Wed, 8 Dec 2021 17:12:54 GMT
• Title: SoK: Vehicle Orientation Representations for Deep Rotation Estimation
• Authors: Huahong Tu, Siyuan Peng, Vladimir Leung, Richard Gao
• Abstract summary: We study the accuracy performance of various existing orientation representations using the KITTI 3D object detection dataset. We propose a new form of orientation representation: Tricosine.
• Score: 2.052323405257355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, an influx of 3D autonomous vehicle object detection algorithms. However, little attention was paid to orientation prediction. Existing research work proposed various prediction methods, but a holistic, conclusive review has not been conducted. Through our experiments, we categorize and empirically 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

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