Enhancing perception performance via multi-agent collaboration has gained increasing attention in the field of autonomous driving. However, as the number of agents grows, the manual annotation required for training collaborative detectors increases significantly. To tackle this problem, we introduce an unsupervised method that learns to Detect Objects from Multi-Agent LiDAR scans, named DOtA, without using labels from external. DOtA first generates preliminary labels by an initial detector, which is trained by internally shared information of collaborative agents. DOtA then optimizes these preliminary labels by utilizing the physical rule constraints derived from the surrounding area of the object. Building on DOtA, we further propose DOtA++, an enhanced version that improves performance by leveraging composite prior constraints. Beyond physical rule constraints, DOtA++ further uses image data as an auxiliary modality to introduce multi-agent observation consistency constraints, boosting object classification, while also incorporating point cloud geometric distribution constraints to improve structural description. Extensive experiments on widely-used benchmarks demonstrate that DOtA and DOtA++ effectively perceive potential objects in the scene without manual annotations. In particular, DOtA++ shows 10.7% mAP improvement over traditional unsupervised methods on V2X-R dataset. Our code is available at https://github.com/xmuqimingxia/DOtAv2.
Xia et al. (Thu,) studied this question.
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