The paper presents a method for creating LiDAR-based 3D object detectors that remain accurate under point‑cloud corruptions while meeting strict latency and memory constraints. It achieves robustness by generating corrupted samples that mimic extreme weather and training detectors to focus on hard samples, guided by perturbation sensitivity estimated from an ensemble of seven heterogeneous detectors. Efficiency is improved by narrowing the network and using offline neighborhood‑density measurements to reduce the number of points processed at each sampling stage, resulting in significant gains in AP under corruption and reductions in inference latency and GPU memory usage across multiple datasets and detector architectures.
By Tingxu Han, Zijie Shen, An Guo, Lyuke Wang, Chunrong Fang, Zhenyu Chen, Wei Song
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:2607. 08391v1 Announce Type: cross Abstract: Making tradeoffs between execution latency and result utility (i.
By Ahmet Soyyigit, Shuochao Yao, Heechul Yun
arXiv:2609.23541v1 Announce Type: new
Abstract: Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and...
By Ziying Song, Lin Liu, Hongyu Pan, Shaoqing Xu, Lei Yang, Mingzhe Guo, Caiyan Jia
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.
By Pu Luo, Cong Xu, Yumei Li, Kexin Zhang, Licheng Jiao, Wenping Ma, Lingling Li
We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026. The detector must be trained only on synthetic data and is evaluated on...
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.
By Debojyoti Biswas, Xianbiao Hu
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.
arXiv:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.
By Yu Han, Zhiwei Huang, Yanting Zhang, Fangjun Ding, Shen Cai, Xiaoyu Tang, Yanchao Dong, Rui Fan
Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bounding box predictions can lead to spurious velocity estimates in tracking and falsely predicted trajectories.
DriveOSS is a new real‑world multimodal dataset and benchmark for 3D anomaly segmentation and open‑set semantic segmentation in autonomous driving. It contains 12,661 synchronized frames from six RGB cameras, a 128‑beam LiDAR, and GPS/IMU, covering 52 driving scenes under various weather conditions. The data are split into in‑distribution (ID) scenes for training and out‑of‑distribution (OOD) scenes for validation and testing, with unknown objects labeled as an outlier class to challenge current 3D perception systems.
By Zhiran Yan, Savankumar Bhanderi, Sourabh Lolge, Rui Song, Girdharbhai Paneliya, Shiva Agrawal, Gordon Elger
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.
By Abdullah Naeem, Anav Katwal, Ayon Dey, Noman Khan, Md Tamjidul Hoque