تقرير
Pillar-based Object Detection for Autonomous Driving
| العنوان: | Pillar-based Object Detection for Autonomous Driving |
|---|---|
| المؤلفون: | Wang, Yue, Fathi, Alireza, Kundu, Abhijit, Ross, David, Pantofaru, Caroline, Funkhouser, Thomas, Solomon, Justin |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Robotics |
| الوصف: | 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. Comment: Accepted to ECCV2020 |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2007.10323Test |
| رقم الانضمام: | edsarx.2007.10323 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |