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arXiv · 2307.07935

S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality

Jinlong Li·Runsheng Xu·Xinyu Liu·Baolu Li·Qin Zou·Jiaqi Ma·Hongkai Yu
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Abstract

Due to the lack of enough real multi-agent data and time-consuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for training and validating. However, the perception performance is degraded when these simulation-trained models are deployed to the real world, due to the significant domain gap between the simulated and real data. In this paper, we propose the first Simulation-to-Reality transfer learning framework for multi-agent cooperative perception using a novel Vision Transformer, named as S2R-ViT, which considers both the Deployment Gap and Feature Gap between simulated and real data. We investigate the effects of these two types of domain gaps and propose a novel uncertainty-aware vision transformer to effectively relief the Deployment Gap and an agent-based feature adaptation module with inter-agent and ego-agent discriminators to reduce the Feature Gap. Our intensive experiments on the public multi-agent cooperative perception datasets OPV2V and V2V4Real demonstrate that the proposed S2R-ViT can effectively bridge the gap from simulation to reality and outperform other methods significantly for point cloud-based 3D object detection.

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Jinlong Li, Runsheng Xu, Xinyu Liu, Baolu Li, Qin Zou, Jiaqi Ma, Hongkai Yu. 2023-07-16. S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality. https://arxiv.org/abs/2307.07935

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