Papers › OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with...
OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication
Runsheng Xu, Hao Xiang, Xin Xia, Xu Han, Jinlong Li, Jiaqi Ma
Employing Vehicle-to-Vehicle communication to enhance perception performance in self-driving technology has attracted considerable attention recently; however, the absence of a suitable open dataset for benchmarking algorithms has made it difficult to develop and assess cooperative perception technologies. To this end, we present the first large-scale open simulated dataset for Vehicle-to-Vehicle perception. It contains over 70 interesting scenes, 11,464 frames, and 232,913 annotated 3D vehicle bounding boxes, collected from 8 towns in CARLA and a digital town of Culver City, Los Angeles. We then construct a comprehensive benchmark with a total of 16 implemented models to evaluate several information fusion strategies~(i.e. early, late, and intermediate fusion) with state-of-the-art LiDAR detection algorithms. Moreover, we propose a new Attentive Intermediate Fusion pipeline to aggregate information from multiple connected vehicles. Our experiments show that the proposed pipeline can be easily integrated with existing 3D LiDAR detectors and achieve outstanding performance even with large compression rates. To encourage more researchers to investigate Vehicle-to-Vehicle perception, we will release the dataset, benchmark methods, and all related codes in https://mobility-lab.seas.ucla.edu/opv2v/.
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | OPV2V | Attentive Fusion (PointPillar backbone) | AP@0.7@CulverCity | 0.735 | #2 of 5 | Archive leaderboard | report |
| 3D Object Detection | OPV2V | Attentive Fusion (PointPillar backbone) | AP@0.7@Default | 0.815 | #2 of 5 | Archive leaderboard | report |
| 3D Object Detection | OPV2V | Late Fusion (PointPillar backbone) | AP@0.7@CulverCity | 0.669 | #5 of 5 | Archive leaderboard | report |
| 3D Object Detection | OPV2V | Late Fusion (PointPillar backbone) | AP@0.7@Default | 0.781 | #5 of 5 | Archive leaderboard | report |
| 3D Object Detection | V2XSet | AttentiveFusion | AP0.5 (Noisy) | 0.709 | #6 of 6 | Archive leaderboard | report |
| 3D Object Detection | V2XSet | AttentiveFusion | AP0.5 (Perfect) | 0.807 | #6 of 6 | Archive leaderboard | report |
| 3D Object Detection | V2XSet | AttentiveFusion | AP0.7 (Noisy) | 0.487 | #6 of 6 | Archive leaderboard | report |
| 3D Object Detection | V2XSet | AttentiveFusion | AP0.7 (Perfect) | 0.664 | #6 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections