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CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Collaborative perception enhances environment understanding through multi-agent information sharing, but its performance in real-world scenarios is constrained by heterogeneous sensor modalities and model architectures. Recent protocol-based two-stage methods alleviate this problem by mapping heterogeneous features into a shared protocol space; however, independently trained modality-specific converters often generate modality-specific pseudo-protocol distributions, leading to semantic inconsistency and error accumulation, which is particularly pronounced in scenarios with large modality discr

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