LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention

• URL: http://arxiv.org/abs/2004.01389v1
• Date: Fri, 3 Apr 2020 06:06:52 GMT
• Title: LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
• Authors: Junbo Yin, Jianbing Shen, Chenye Guan, Dingfu Zhou, Ruigang Yang
• Abstract summary: 3D object detectors usually focus on the single-frame detection, while ignoring the information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point sequences.
• Score: 100.52873557168637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while ignoring the spatiotemporal information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences. The proposed model comprises a spatial feature encoding component and a spatiotemporal feature aggregation component. In the former component, a novel Pillar Message Passing Network (PMPNet) is proposed to encode each discrete point cloud frame. It adaptively collects information for a pillar node from its neighbors by iterative message passing, which effectively enlarges the receptive field of the pillar feature. In the latter component, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU) to aggregate the spatiotemporal information, which enhances the conventional ConvGRU with an attentive memory gating mechanism. AST-GRU contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module, which can emphasize the foreground objects and align the dynamic objects, respectively. Experimental results demonstrate that the proposed 3D video object detector achieves state-of-the-art performance on the large-scale nuScenes benchmark.

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