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LoDA: A Level of Detection Aware Method and a Multimodal Sensing Benchmark for Object Level Change Detection — arXiv:2608.05356

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05356
License (as archived): CC0-1.0
Archived: 2026-10-31T10:35:53.240Z
SHA-256 of the archived text: e172857175f5b1c4e2f88111dbdac77ec025adc8eeb56999dfc6c1d8f83246a7
Stable URL of this page: https://metraipolis.com/documento/dc_f202000fdf4f

How to cite

LoDA: A Level of Detection Aware Method and a Multimodal Sensing Benchmark for Object Level Change Detection — arXiv:2608.05356 — arXiv (en), https://arxiv.org/abs/2608.05356 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f202000fdf4f (Metraipolis archive dc_f202000fdf4f, sha256 e172857175f5b1c4e2f88111dbdac77ec025adc8eeb56999dfc6c1d8f83246a7)

BibTeX

@misc{dcf202000fdf4f,
  title = {LoDA: A Level of Detection Aware Method and a Multimodal Sensing Benchmark for Object Level Change Detection — arXiv:2608.05356},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05356},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f202000fdf4f; sha256 e172857175f5b1c4e2f88111dbdac77ec025adc8eeb56999dfc6c1d8f83246a7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05356. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

High-definition 3D LiDAR maps are important for autonomous driving and smart-city services, which require reliable detection of object-level changes in multi-temporal urban LiDAR to keep digital maps aligned with the physical world. Existing approaches from raster height differencing to depth image and point-cloud networks often remain tile-based and threshold-driven, yielding per-point scores without explicit detection limits or consistent object-level labels. We propose an object-level 3D change-detection pipeline that integrates detection-limit-aware registration, geometry-driven object proxies with rule-based semantic and instance segmentation, and displacement cues in height, volume, and surface-normal direction to assign five change labels with confidence. By decoupling registration, geometry, and semantics, the pipeline propagates pose uncertainty into spatially varying detection limits, stabilizes cross-epoch correspondences, and suppresses false changes caused by residual misalignment and density variation. We also present LoDA, a level-of-detection (LoD) aware benchmark for the Subiaco district with fused multi-temporal vehicle-LiDAR maps constructed with LiDAR, GNSS, and IMU support, semantic instances, and object-level annotations. On this benchmark, our method achieves 95.0% accuracy, 90.8% macro F1, and 83.0% macro IoU, exceeding the best baseline by 8.7 IoU points and 4.4 F1 points. On the public Urb3DCD-V2 benchmark evaluated under the official point-wise protocol, it reaches 96.81% mean accuracy and 89.52% mean change IoU, improving over the strongest reported baselines by 1.36 points in mAcc and 3.18 points in mIoUch.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-07 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2608.05356 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T10:23:30.968Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).