用无人机扩展车载感知，解决遮挡盲区问题。
OpenCOOD-Air: Prompting Heterogeneous Ground-Air Collaborative Perception with Spatial Conversion and Offset Prediction
- 引入无人机协同，通过空间转换与偏移预测实现跨域融合
- 2D/3D [email protected]提升4%/7%，优于现有方法
- 适合智能交通、自动驾驶中多源感知场景
尽管车车协同（V2V）通过多智能体数据共享扩展了感知范围，但其可靠性仍受地面遮挡和车载传感器视角局限的严重制约，常导致关键感知盲区。本文提出OpenCOOD-Air框架，将无人机作为可扩展平台融入V2V协同感知，以克服上述限制。为缓解地面-空中域间梯度干扰和数据稀疏问题，采用迁移学习策略，从预训练的V2V模型微调无人机权重。为防止该转换过程中的空间信息丢失，将地面-空中协同感知建模为带显式高度监督的异构融合任务，提出跨域空间转换器（CDSC）与空间偏移预测变压器（SOPT）。此外，构建了OPV2V-Air基准测试集，验证从V2V到车-车-无人机的演进可行性。相比最先进方法，本方案在2D和3D [email protected]上分别提升4%和7%。
原文摘要 · Abstract (English)
While Vehicle-to-Vehicle (V2V) collaboration extends sensing ranges through multi-agent data sharing, its reliability remains severely constrained by ground-level occlusions and the limited perspective of chassis-mounted sensors, which often result in critical perception blind spots. We propose OpenCOOD-Air, a novel framework that integrates UAVs as extensible platforms into V2V collaborative perception to overcome these constraints. To mitigate gradient interference from ground-air domain gaps and data sparsity, we adopt a transfer learning strategy to fine-tune UAV weights from pre-trained V2V models. To prevent the spatial information loss inherent in this transition, we formulate ground-air collaborative perception as a heterogeneous integration task with explicit altitude supervision and introduce a Cross-Domain Spatial Converter (CDSC) and a Spatial Offset Prediction Transformer (SOPT). Furthermore, we present the OPV2V-Air benchmark to validate the transition from V2V to Vehicle-to-Vehicle-to-UAV. Compared to state-of-the-art methods, our approach improves 2D and 3D [email protected] by 4% and 7%, respectively.
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