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137722 |
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|a Wang, Y
|e author
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|a Fathi, A
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|a Kundu, A
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|a Ross, DA
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|a Pantofaru, C
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|a Funkhouser, T
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|a Solomon, J
|e author
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|a Pillar-Based Object Detection for Autonomous Driving
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| 260 |
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|b Springer International Publishing,
|c 2021-11-08T17:29:30Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/137722
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|a © 2020, Springer Nature Switzerland AG. 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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|a en
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|a Article
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| 773 |
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|t 10.1007/978-3-030-58542-6_2
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| 773 |
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|t Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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