# DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model

arXiv: https://arxiv.org/abs/2409.19592 · HTML: https://mlsanity.com/paper/2409.19592/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 30 of 30)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [DDP: Diffusion Model for Dense Visual Prediction](https://mlsanity.com/paper/2303.17559/) (2023)
- [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020)
- [Collaboration Helps Camera Overtake LiDAR in 3D Detection](https://mlsanity.com/paper/2303.13560/) (2023)
- [Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2311.01682/) (2023)
- [DiffBEV: Conditional Diffusion Model for Bird's Eye View Perception](https://mlsanity.com/paper/2303.08333/) (2023)
- [DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving](https://mlsanity.com/paper/2207.07609/) (2022)
- [DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation](https://mlsanity.com/paper/2404.04629/) (2024)
- [A Cooperative Perception System Robust to Localization Errors](https://mlsanity.com/paper/2210.06289/) (2022)
- [Collaborative Semantic Communication for Edge Inference](https://mlsanity.com/paper/2301.03996/) (2023)
- [SceneSense: Diffusion Models for 3D Occupancy Synthesis from Partial Observation](https://mlsanity.com/paper/2403.11985/) (2024)
- [Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems](https://mlsanity.com/paper/2406.03086/) (2024)
- [Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference](https://mlsanity.com/paper/2304.08221/) (2023)
- [Diffusion-Based Particle-DETR for BEV Perception](https://mlsanity.com/paper/2312.11578/) (2023)

## Built on by (most cited first, 6 of 6)

- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Compression Beyond Pixels: Semantic Compression with Multimodal Foundation Models](https://mlsanity.com/paper/2509.05925/) (2025)
- [JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception](https://mlsanity.com/paper/2511.17843/) (2025)
- [Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications](https://mlsanity.com/paper/2511.08416/) (2025)
- [DRCP: Diffusion on Reinforced Cooperative Perception for Perceiving Beyond Limits](https://mlsanity.com/paper/2509.24903/) (2025)
- [WaveComm: Lightweight Communication for Collaborative Perception via Wavelet Feature Distillation](https://mlsanity.com/paper/2603.13365/) (2026)

## Abstract (arXiv)

Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. However, current wireless communication systems are unable to support feature-level and raw-level collaborative algorithms due to their enormous bandwidth demands. In this paper, we propose DiffCP, a novel CP paradigm that utilizes a specialized diffusion model to efficiently compress the sensing information of collaborators. By incorporating both geometric and semantic conditions into the generative model, DiffCP enables feature-level collaboration with an ultra-low communication cost, advancing the practical implementation of CP systems. This paradigm can be seamlessly integrated into existing CP algorithms to enhance a wide range of downstream tasks. Through extensive experimentation, we investigate the trade-offs between communication, computation, and performance. Numerical results demonstrate that DiffCP can significantly reduce communication costs by 14.5-fold while maintaining the same performance as the state-of-the-art algorithm.