The paper explores the importance of collaborative views in multi-agent perception, focusing on maximizing mutual information. It introduces CMiMC, a framework that preserves discriminative information while enhancing collaborative views' efficacy. By defining multi-view mutual information (MVMI), CMiMC improves average precision by 3.08% and 4.44% at IoU thresholds of 0.5 and 0.7, respectively. The method reduces communication volume significantly while maintaining performance comparable to the state-of-the-art.
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