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Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels

Abstract

Unsupervised 3D object detection serves as an important solution for offline 3D object annotation. However, due to the data sparsity and limited views, the clustering-based label fitting in unsupervised object detection often generates low-quality pseudo-labels. Multi-agent collaborative dataset, which involves the sharing of complementary observations among agents, holds the potential to break through this bottleneck. In this paper, we introduce a novel unsupervised method that learns to Detect Objects from Multi-Agent LiDAR scans, termed DOtA, without using labels from external. DOtA first uses the internally shared ego-pose and ego-shape of collaborative agents to initialize the detector, leveraging the generalization performance of neural networks to infer preliminary labels. Subsequently,DOtA uses the complementary observations between agents to perform multi-scale encoding on preliminary labels, then decodes high-quality and low-quality labels. These labels are further used as prompts to guide a correct feature learning process, thereby enhancing the performance of the unsupervised object detection task. Extensive experiments on the V2V4Real and OPV2V datasets show that our DOtA outperforms state-of-the-art unsupervised 3D object detection methods. Additionally, we also validate the effectiveness of the DOtA labels under various collaborative perceptionthis http URLcode is available atthis https URL.

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@article{xia2025_2503.08421,
  title={ Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels },
  author={ Qiming Xia and Wenkai Lin and Haoen Xiang and Xun Huang and Siheng Chen and Zhen Dong and Cheng Wang and Chenglu Wen },
  journal={arXiv preprint arXiv:2503.08421},
  year={ 2025 }
}
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