# Learning from Distributed Eyes: Leveraging Collaborative Perception for Automated Model Adaptation

arXiv: https://arxiv.org/abs/2609.18511 · HTML: https://mlsanity.com/paper/2609.18511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 37 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 28 of 28)

- [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)
- [MEMO: Test Time Robustness via Adaptation and Augmentation](https://mlsanity.com/paper/2110.09506/) (2021)
- [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)
- [Revisiting Batch Normalization For Practical Domain Adaptation](https://mlsanity.com/paper/1603.04779/) (2016)
- [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)
- [Parameter-free Online Test-time Adaptation](https://mlsanity.com/paper/2201.05718/) (2022)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [Train in Germany, Test in The USA: Making 3D Object Detectors Generalize](https://mlsanity.com/paper/2005.08139/) (2020)
- [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020)
- [Automatic adaptation of object detectors to new domains using self-training](https://mlsanity.com/paper/1904.07305/) (2019)
- [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025)
- [Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities](https://mlsanity.com/paper/2401.01544/) (2024)
- [Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving](https://mlsanity.com/paper/2310.00013/) (2023)
- [PACP: Priority-Aware Collaborative Perception for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2404.06891/) (2024)
- [Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities](https://mlsanity.com/paper/2304.11397/) (2023)
- [Directed-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention](https://mlsanity.com/paper/2409.08840/) (2024)
- [Unsupervised Adaptation from Repeated Traversals for Autonomous Driving](https://mlsanity.com/paper/2303.15286/) (2023)
- [R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communications](https://mlsanity.com/paper/2410.04168/) (2024)
- [Curricular Object Manipulation in LiDAR-based Object Detection](https://mlsanity.com/paper/2304.04248/) (2023)
- [Semi-Automatic Annotation For Visual Object Tracking](https://mlsanity.com/paper/2101.06977/) (2021)
- [Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels](https://mlsanity.com/paper/2503.08421/) (2025)
- [Learning 3D Perception from Others' Predictions](https://mlsanity.com/paper/2410.02646/) (2024)
- [Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Object Detection in Autonomous Driving](https://mlsanity.com/paper/2503.17697/) (2025)
- [Birdcast: Interest-aware BEV Multicasting for Infrastructure-assisted Collaborative Perception](https://mlsanity.com/paper/2604.00701/) (2026)
- [Update the Unseen Only: Minimizing AoI for Collaborative Perception through Online Learning](https://mlsanity.com/paper/2607.20967/) (2026)

## Built on by (most cited first, 1 of 1)

- [Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement](https://mlsanity.com/paper/2610.15846/) (2026)

## Abstract (arXiv)

In autonomous driving, perception models often struggle to generalize to new environments due to domain shifts. While unsupervised model adaptation offers a feasible solution without labor-intensive manual labeling, existing methods that rely solely on the ego-vehicle's data often lead to inferior pseudo-labeling performance. To address this critical issue, we propose LDE, Learning from Distributed ``Eyes", a novel framework that transforms collaborative perception (CP) into a source of high-quality supervision for model adaptation. This pseudo-labeling approach is hyperparameter-insensitive and relatively reliable, assuming CP often outperforms single-agent's perception. However, naively implementing this approach encounters (1) the communication bottleneck of sharing rich features under time and bandwidth constraints, (2) the view discrepancy between the CP view and the learner's Field of View (FoV), and (3) the unreliability even in CP-generated labels. To address these issues, we design an adaptation-oriented feature sharing mechanism that selectively transmits the most critical information for adaptation, an FoV filtering method that meticulously eliminates mismatched labels, and a curriculum learning strategy to progressively exploit pseudo labels. Extensive experiments on 3D object detection tasks demonstrate that LDE consistently outperforms both the pre-trained models and state-of-the-art unsupervised adaptation methods.