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AFDet: Anchor Free One Stage 3D Object Detection

About

High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving. Most previous works try to solve it using anchor-based detection methods which come with two drawbacks: post-processing is relatively complex and computationally expensive; tuning anchor parameters is tricky. We are the first to address these drawbacks with an anchor free and Non-Maximum Suppression free one stage detector called AFDet. The entire AFDet can be processed efficiently on a CNN accelerator or a GPU with the simplified post-processing. Without bells and whistles, our proposed AFDet performs competitively with other one stage anchor-based methods on KITTI validation set and Waymo Open Dataset validation set.

Runzhou Ge, Zhuangzhuang Ding, Yihan Hu, Yu Wang, Sijia Chen, Li Huang, Yuan Li• 2020

Related benchmarks

TaskDatasetResultRank
3D Object DetectionWaymo Open Dataset (val)--
175
3D Object DetectionWaymo Open Dataset LEVEL_1 (val)
3D AP63.69
46
3D Object Detection (Vehicle)Waymo Open Dataset LEVEL_1 (val)
3D AP Overall63.69
34
3D Vehicle DetectionWaymo Open Dataset v1.2 (val)
L1 3D mAP63.69
29
Vehicle DetectionWaymo Open Dataset LEVEL_1 v1.2 (val)
3D AP63.69
28
3D Object Detection (Vehicle)Waymo Open Dataset (val)--
14
3D Object DetectionWaymo Open Dataset Vehicles (val)--
13
3D Object DetectionWaymo Open Dataset 202 sequences (val)
L1 3D mAP Overall63.69
6
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