# JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception

Source: [JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception](<https://www.sota2.com/research/paper/6925384dd6b0dd334292f67d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-17T05:00:04.157Z

Authors: Chenyi Wang, Zhaowei Li, Ming F. Li, Wujie Wen

Published: 2025-11-21T23:36:24.000Z

## Abstract

Multi-agent cooperative perception (CP) promises to overcome the inherent occlusion and range limitations of single-agent systems in autonomous driving, yet its practicality is severely constrained by limited Vehicle-to-Everything (V2X) communication bandwidth. Existing approaches attempt to improve bandwidth efficiency via compression or heuristic message selection, but neglect the semantic relevance and cross-agent redundancy of the transmitted data. In this paper, we formulate a joint semantic feature encoding and transmission problem that maximizes CP accuracy under a communication budget, and introduce JigsawComm, an end-to-end semantic-aware framework that learns to \`\`assemble the puzzle'' of multi-agent feature transmission. JigsawComm uses a regularized encoder to extract \\emph{sparse, semantically relevant features}, and a lightweight Feature Utility Estimator (FUE) to predict each agent's per-cell contribution to the downstream perception task. The FUE-generated compact meta utility maps are exchanged among agents and used to compute an optimal transmission policy under the learned utility proxy. This policy inherently \\emph{eliminates cross-agent redundancy}, bounding the feature transmission payload to $\\mathcal{O}(1)$ as the number of agents grows, while the meta information overhead remains negligible. The whole pipeline is trained end-to-end through a differentiable scheduling module, informing the FUE to be aligned with the task objective. On the OPV2V and DAIR-V2X benchmarks, JigsawComm reduces total data volume by over 20--500${\\times}$ while matching or exceeding the accuracy of state-of-the-art methods.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [3D Object Detection on DAIR-V2X (Detection & Efficiency)](<https://www.sota2.com/research/sota/3d-object-detection-on-dair-v2x-detection-and-efficiency.md>): 3D Object Detection; DAIR-V2X; mAP@.5: 69.

- [3D Object Detection on OPV2V](<https://www.sota2.com/research/sota/3d-object-detection-on-opv2v.md>): 3D Object Detection; OPV2V; AP@0.50: 92.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.17843>)

[PDF](<https://arxiv.org/pdf/2511.17843.pdf>)