DeepInteraction: 3D Object Detection via Modality Interaction

Existing top-performance 3D object detectors typically rely on the multi-modal fusion strategy. This design is however fundamentally restricted due to overlooking the modality-specific useful information and finally hampering the model performance. To address this limitation, in this work we introduce a novel modality interaction strategy where individual per-modality representations are learned and maintained throughout for enabling their unique characteristics to be exploited during object detection. To realize this proposed strategy, we design a DeepInteraction architecture characterized by a multi-modal representational interaction encoder and a multi-modal predictive interaction decoder. Experiments on the large-scale nuScenes dataset show that our proposed method surpasses all prior arts often by a large margin. Crucially, our method is ranked at the first position at the highly competitive nuScenes object detection leaderboard.

Authors

• Zeyu Yang The Chinese University of Hong Kong, Shenzhen Shenzhen Loop Area Institute
• Jiaqi Chen The University of Hong Kong
• Zhenwei Miao Alibaba DAMO Academy
• Wei Li
• Xiatian Zhu University of Surrey
• Li Zhang

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