# Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors

arXiv: https://arxiv.org/abs/1912.12147 · HTML: https://mlsanity.com/paper/1912.12147/

## Key facts

- Cited by 110 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 30 of 110)

- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [Robust Collaborative 3D Object Detection in Presence of Pose Errors](https://mlsanity.com/paper/2211.07214/) (2022)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication](https://mlsanity.com/paper/2212.08273/) (2022)
- [Uncertainty Quantification of Collaborative Detection for Self-Driving](https://mlsanity.com/paper/2209.08162/) (2022)
- [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025)
- [CORE: Cooperative Reconstruction for Multi-Agent Perception](https://mlsanity.com/paper/2307.11514/) (2023)
- [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://mlsanity.com/paper/2308.16714/) (2023)
- [RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception](https://mlsanity.com/paper/2403.10145/) (2024)
- [Among Us: Adversarially Robust Collaborative Perception by Consensus](https://mlsanity.com/paper/2303.09495/) (2023)
- [Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2311.01682/) (2023)
- [Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds](https://mlsanity.com/paper/2208.00116/) (2022)
- [CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization](https://mlsanity.com/paper/2503.03430/) (2025)
- [Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation](https://mlsanity.com/paper/2303.14346/) (2023)
- [PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR](https://mlsanity.com/paper/2203.06319/) (2022)
- [Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction](https://mlsanity.com/paper/2303.10552/) (2023)
- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [LangCoop: Collaborative Driving with Language](https://mlsanity.com/paper/2504.13406/) (2025)
- [Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles](https://mlsanity.com/paper/2103.03786/) (2021)
- [QUEST: Query Stream for Practical Cooperative Perception](https://mlsanity.com/paper/2308.01804/) (2023)
- [Fast and Robust Registration of Partially Overlapping Point Clouds](https://mlsanity.com/paper/2112.09922/) (2021)
- [MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings](https://mlsanity.com/paper/2310.00400/) (2023)
- [A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation](https://mlsanity.com/paper/2208.10590/) (2022)
- [DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception](https://mlsanity.com/paper/2310.08117/) (2023)
- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception](https://mlsanity.com/paper/2404.10540/) (2024)
- [One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2411.16799/) (2024)

## Abstract (arXiv)

3D object detection is a common function within the perception system of an autonomous vehicle and outputs a list of 3D bounding boxes around objects of interest. Various 3D object detection methods have relied on fusion of different sensor modalities to overcome limitations of individual sensors. However, occlusion, limited field-of-view and low-point density of the sensor data cannot be reliably and cost-effectively addressed by multi-modal sensing from a single point of view. Alternatively, cooperative perception incorporates information from spatially diverse sensors distributed around the environment as a way to mitigate these limitations. This article proposes two schemes for cooperative 3D object detection using single modality sensors. The early fusion scheme combines point clouds from multiple spatially diverse sensing points of view before detection. In contrast, the late fusion scheme fuses the independently detected bounding boxes from multiple spatially diverse sensors. We evaluate the performance of both schemes, and their hybrid combination, using a synthetic cooperative dataset created in two complex driving scenarios, a T-junction and a roundabout. The evaluation shows that the early fusion approach outperforms late fusion by a significant margin at the cost of higher communication bandwidth. The results demonstrate that cooperative perception can recall more than 95% of the objects as opposed to 30% for single-point sensing in the most challenging scenario. To provide practical insights into the deployment of such system, we report how the number of sensors and their configuration impact the detection performance of the system.