SimFuse3D tackles cross‑platform LiDAR unsupervised domain adaptation by addressing box‑point inconsistency through source‑guided target simulation and confidence‑guided multi‑stage localization reweighting. It preserves target placement, repairs pseudo‑objects using labeled source geometry, and reweights predictions based on confidence, all during adaptation without altering the detector architecture. The method outperforms existing adaptation techniques across six cross‑platform transfers and ranks first on nuScenes‑to‑KITTI for both evaluated detectors.
arXiv:2608. 07579v1 Announce Type: cross Abstract: The AI City Challenge 2026 Track 1 evaluates multi-camera 3D perception in large indoor warehouses under a synthetic-to-real (Sim2Real) setting; depth is available only for training and validation, so inference is RGB-only.
arXiv:2607. 02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse.
Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical deployment. Recent w...
arXiv:2607. 05955v1 Announce Type: cross Abstract: Interactive 3D segmentation aims to extract object masks in point clouds with minimal user clicks.
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
Recent advancements in LiDAR-only 3D object detection have demonstrated improved detection accuracy over benchmark datasets. However, the adversarial robustness of these models remains untested.
arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.
arXiv:2607. 26645v1 Announce Type: cross Abstract: Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion.
arXiv:2404.09431v3 Announce Type: replace Abstract: Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations th...
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
arXiv:2606. 03568v1 Announce Type: cross Abstract: Post-processing is a critical stage in LiDAR-based 3D object detection, where dense and overlapping proposals must be filtered for compact and reliable perception.
The paper presents a solution for the UCF UrbanTwin LUMPI Track in the Sim-to-Real LiDAR Challenge, where a detector trained solely on synthetic data must perform on real LiDAR frames. The approach tackles the Sim2Real gap through data alignment, diversified sampling, augmentation, and specialized detectors, followed by class-aware fusion and calibration techniques. The final submission achieved a Combined Score of 0.4692, a Detection Score of 0.1797, a Realism Score of 0.9035, and a 3D mAP@0.5 of 0.1258.