# BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities

arXiv: https://arxiv.org/abs/2310.14702 · HTML: https://mlsanity.com/paper/2310.14702/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 29 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [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)
- [Learning Distilled Collaboration Graph for Multi-Agent Perception](https://mlsanity.com/paper/2111.00643/) (2021)
- [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)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [Robust Collaborative 3D Object Detection in Presence of Pose Errors](https://mlsanity.com/paper/2211.07214/) (2022)
- [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022)
- [Latency-Aware Collaborative Perception](https://mlsanity.com/paper/2207.08560/) (2022)
- [Collaboration Helps Camera Overtake LiDAR in 3D Detection](https://mlsanity.com/paper/2303.13560/) (2023)
- [HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer](https://mlsanity.com/paper/2304.10628/) (2023)
- [Learning to Communicate and Correct Pose Errors](https://mlsanity.com/paper/2011.05289/) (2020)
- [Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving](https://mlsanity.com/paper/2109.11615/) (2021)
- [Multi-Robot Collaborative Perception with Graph Neural Networks](https://mlsanity.com/paper/2201.01760/) (2022)
- [Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking](https://mlsanity.com/paper/2107.00771/) (2021)

## Built on by (most cited first, 20 of 20)

- [V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection](https://mlsanity.com/paper/2411.08402/) (2024)
- [One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2411.16799/) (2024)
- [A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges](https://mlsanity.com/paper/2404.16139/) (2024)
- [Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models](https://mlsanity.com/paper/2408.02408/) (2024)
- [Pragmatic Heterogeneous Collaborative Perception via Generative Communication Mechanism](https://mlsanity.com/paper/2510.19618/) (2025)
- [SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception](https://mlsanity.com/paper/2508.13007/) (2025)
- [EIMC: Efficient Instance-aware Multi-modal Collaborative Perception](https://mlsanity.com/paper/2603.02532/) (2026)
- [InfoCom: Kilobyte-Scale Communication-Efficient Collaborative Perception with Information Bottleneck](https://mlsanity.com/paper/2512.10305/) (2025)
- [EffiComm: Bandwidth Efficient Multi Agent Communication](https://mlsanity.com/paper/2507.19354/) (2025)
- [COOPERTRIM: Adaptive Data Selection for Uncertainty-Aware Cooperative Perception](https://mlsanity.com/paper/2602.13287/) (2026)
- [RG-Attn: Radian Glue Attention for Multi-modality Multi-agent Cooperative Perception](https://mlsanity.com/paper/2501.16803/) (2025)
- [LCV2I: Communication-Efficient and High-Performance Collaborative Perception Framework with Low-Resolution LiDAR](https://mlsanity.com/paper/2502.17039/) (2025)
- [SiMO: Single-Modality-Operable Multimodal Collaborative Perception](https://mlsanity.com/paper/2603.08240/) (2026)
- [4D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weather](https://mlsanity.com/paper/2606.00416/) (2026)
- [C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation](https://mlsanity.com/paper/2607.01827/) (2026)
- [DRCP: Diffusion on Reinforced Cooperative Perception for Perceiving Beyond Limits](https://mlsanity.com/paper/2509.24903/) (2025)
- [UECP: Uncertainty-Enhanced Collaborative Perception](https://mlsanity.com/paper/2606.23046/) (2026)
- [CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentation](https://mlsanity.com/paper/2608.14085/) (2026)
- [CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2609.03818/) (2026)
- [LR-V2X: Loss-Resilient Collaborative Perception under Low-Bandwidth Communication](https://mlsanity.com/paper/2610.11264/) (2026)

## Abstract (arXiv)

Collaborative perception enables agents to share complementary perceptual information with nearby agents. This would improve the perception performance and alleviate the issues of single-view perception, such as occlusion and sparsity. Most existing approaches mainly focus on single modality (especially LiDAR), and not fully exploit the superiority of multi-modal perception. We propose a collaborative perception paradigm, BM2CP, which employs LiDAR and camera to achieve efficient multi-modal perception. It utilizes LiDAR-guided modal fusion, cooperative depth generation and modality-guided intermediate fusion to acquire deep interactions among modalities of different agents, Moreover, it is capable to cope with the special case where one of the sensors, same or different type, of any agent is missing. Extensive experiments validate that our approach outperforms the state-of-the-art methods with 50X lower communication volumes in both simulated and real-world autonomous driving scenarios. Our code is available at https://github.com/byzhaoAI/BM2CP.