Pillar-based Object Detection for Autonomous Driving

• URL: http://arxiv.org/abs/2007.10323v2
• Date: Sun, 26 Jul 2020 21:13:04 GMT
• Title: Pillar-based Object Detection for Autonomous Driving
• Authors: Yue Wang, Alireza Fathi, Abhijit Kundu, David Ross, Caroline Pantofaru, Thomas Funkhouser, Justin Solomon
• Abstract summary: 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 issue caused by anchors. 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.
• Score: 33.021347169775474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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