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arXiv · 2007.10323

Pillar-based Object Detection for Autonomous Driving

Yue Wang·Alireza Fathi·Abhijit Kundu·David Ross·Caroline Pantofaru·Thomas Funkhouser·Justin Solomon
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Abstract

We present a simple and flexible object detection framework optimized for autonomous driving. Building on the observation that point clouds in this application are extremely sparse, we propose a practical pillar-based approach to fix the imbalance issue caused by anchors. In particular, our algorithm incorporates a cylindrical projection into multi-view feature learning, predicts bounding box parameters per pillar rather than per point or per anchor, and includes an aligned pillar-to-point projection module to improve the final prediction. Our anchor-free approach avoids hyperparameter search associated with past methods, simplifying 3D object detection while significantly improving upon state-of-the-art.

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Yue Wang, Alireza Fathi, Abhijit Kundu, David Ross, Caroline Pantofaru, Thomas Funkhouser, Justin Solomon. 2020-07-20. Pillar-based Object Detection for Autonomous Driving. https://arxiv.org/abs/2007.10323

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