SiMO: Single-Modality-Operable Multimodal Collaborative Perception

• URL: http://arxiv.org/abs/2603.08240v1
• Date: Mon, 09 Mar 2026 11:09:53 GMT
• Title: SiMO: Single-Modality-Operable Multimodal Collaborative Perception
• Abstract summary: This paper introduces Single-Modality-Operable Multimodal Collaborative Perception (SiMO)<n>By adopting the proposed Length-Adaptive Multi-Modal Fusion (LAMMA), SiMO can adaptively handle remaining modal features during modal failures.<n>Experiments demonstrate that SiMO effectively aligns multimodal features while simultaneously preserving modality-specific features.
• Score: 21.057627256755385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative perception integrates multi-agent perspectives to enhance the sensing range and overcome occlusion issues. While existing multimodal approaches leverage complementary sensors to improve performance, they are highly prone to failure--especially when a key sensor like LiDAR is unavailable. The root cause is that feature fusion leads to semantic mismatches between single-modality features and the downstream modules. This paper addresses this challenge for the first time in the field of collaborative perception, introducing Single-Modality-Operable Multimodal Collaborative Perception (SiMO). By adopting the proposed Length-Adaptive Multi-Modal Fusion (LAMMA), SiMO can adaptively handle remaining modal features during modal failures while maintaining consistency of the semantic space. Additionally, leveraging the innovative "Pretrain-Align-Fuse-RD" training strategy, SiMO addresses the issue of modality competition--generally overlooked by existing methods--ensuring the independence of each individual modality branch. Experiments demonstrate that SiMO effectively aligns multimodal features while simultaneously preserving modality-specific features, enabling it to maintain optimal performance across all individual modalities. The implementation details can be found in https://github.com/dempsey-wen/SiMO.

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