InfoCom: Kilobyte-Scale Communication-Efficient Collaborative Perception with Information Bottleneck

• URL: http://arxiv.org/abs/2512.10305v1
• Date: Thu, 11 Dec 2025 05:51:02 GMT
• Title: InfoCom: Kilobyte-Scale Communication-Efficient Collaborative Perception with Information Bottleneck
• Authors: Quanmin Wei, Penglin Dai, Wei Li, Bingyi Liu, Xiao Wu,
• Abstract summary: InfoCom is an information-aware framework establishing the pioneering theoretical foundation for communication-efficient collaborative perception.<n>Experiments across multiple datasets demonstrate that InfoCom achieves near-lossless perception while reducing communication overhead from megabyte to kilobyte-scale, representing 440-fold and 90-fold reductions per agent compared to Where2comm and ERMVP, respectively.
• Score: 14.691852809650323
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Precise environmental perception is critical for the reliability of autonomous driving systems. While collaborative perception mitigates the limitations of single-agent perception through information sharing, it encounters a fundamental communication-performance trade-off. Existing communication-efficient approaches typically assume MB-level data transmission per collaboration, which may fail due to practical network constraints. To address these issues, we propose InfoCom, an information-aware framework establishing the pioneering theoretical foundation for communication-efficient collaborative perception via extended Information Bottleneck principles. Departing from mainstream feature manipulation, InfoCom introduces a novel information purification paradigm that theoretically optimizes the extraction of minimal sufficient task-critical information under Information Bottleneck constraints. Its core innovations include: i) An Information-Aware Encoding condensing features into minimal messages while preserving perception-relevant information; ii) A Sparse Mask Generation identifying spatial cues with negligible communication cost; and iii) A Multi-Scale Decoding that progressively recovers perceptual information through mask-guided mechanisms rather than simple feature reconstruction. Comprehensive experiments across multiple datasets demonstrate that InfoCom achieves near-lossless perception while reducing communication overhead from megabyte to kilobyte-scale, representing 440-fold and 90-fold reductions per agent compared to Where2comm and ERMVP, respectively.

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