PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud

• URL: http://arxiv.org/abs/2112.15458v1
• Date: Fri, 31 Dec 2021 13:41:37 GMT
• Title: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud
• Authors: Duy-Tho Le, Hengcan Shi, Hamid Rezatofighi, Jianfei Cai
• Abstract summary: We present PiFeNet, an efficient real-time 3D detector for pedestrian detection from point clouds. We address two challenges that 3D object detection frameworks encounter when detecting pedestrians: low of pillar features and small occupation areas of pedestrians in point clouds. Our approach is ranked 1st in KITTI pedestrian BEV and 3D leaderboards while running at 26 frames per second (FPS), and achieves state-of-the-art performance on Nuscenes detection benchmark.
• Score: 64.12626752721766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present PiFeNet, an efficient and accurate real-time 3D detector for pedestrian detection from point clouds. We address two challenges that 3D object detection frameworks encounter when detecting pedestrians: low expressiveness of pillar features and small occupation areas of pedestrians in point clouds. Firstly, we introduce a stackable Pillar Aware Attention (PAA) module for enhanced pillar features extraction while suppressing noises in the point clouds. By integrating multi-point-aware-pooling, point-wise, channel-wise, and task-aware attention into a simple module, the representation capabilities are boosted while requiring little additional computing resources. We also present Mini-BiFPN, a small yet effective feature network that creates bidirectional information flow and multi-level cross-scale feature fusion to better integrate multi-resolution features. Our approach is ranked 1st in KITTI pedestrian BEV and 3D leaderboards while running at 26 frames per second (FPS), and achieves state-of-the-art performance on Nuscenes detection benchmark.

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