Benchmarking domain adaptation for LiDAR-based 3D object detection in autonomous driving

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dc.contributor.authorBalim, Mustafa Alper
dc.contributor.authorHanilci, Cemal
dc.contributor.authorAcir, Nurettin
dc.date.accessioned2026-02-08T15:15:01Z
dc.date.available2026-02-08T15:15:01Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe generalization capability of 3D object detection models is crucial for ensuring robust perception in autonomous driving systems. While state-of-the-art models such as Voxel R-CNN, PV-RCNN, and CenterPoint have demonstrated strong performance on publicly available datasets (e.g., KITTI, Waymo, and nuScenes). In this study, we conduct a comprehensive benchmark evaluation. We introduce two custom datasets: (i) a real-world dataset collected using KARSAN's autonomous minibus equipped with a 128-channel LiDAR sensor under diverse traffic conditions, and (ii) a simulated dataset generated using the AWSIM simulation platform, capturing over five hours of synthetic driving data with virtual LiDAR sensors. Our results indicate that 3D object detection performance is highly dataset-dependent, as no single model achieves superior results across all datasets and metrics. Cross-dataset evaluation highlights the challenges of domain mismatch, which causes significant performance degradation when models are tested on our custom datasets, particularly in the synthetic domain. To mitigate these effects, we explore six domain adaptation techniques and demonstrate that their application substantially improves model performance. Bi3D, SESS, and Uni3D outperform UDA, CLUE, and ST3D, yielding more robust generalization across both real-world and simulated environments. These findings shed light on the potential of domain adaptation to improve model performance across domain shifts, despite the ongoing challenges in achieving consistent outcomes across all environments.
dc.identifier.doi10.1007/s11760-025-04580-z
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue12
dc.identifier.scopus2-s2.0-105015076209
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s11760-025-04580-z
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5561
dc.identifier.volume19
dc.identifier.wosWOS:001567113700029
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectAutonomous driving
dc.subjectLiDAR
dc.subjectDomain adaptation
dc.subject3D object detection
dc.subjectPerception
dc.titleBenchmarking domain adaptation for LiDAR-based 3D object detection in autonomous driving
dc.typeArticle

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