SARFusion introduces a scene-aware routing approach for camera‑LiDAR 3D object detection, decoupling object‑query decoding into separate camera, LiDAR, and fusion branches. By estimating a global scene reliability prior and incorporating object‑level evidence, each query is routed to the most suitable branch, reducing cross‑modal interference. The method achieves strong performance on the nuScenes test set (72.5 mAP, 74.4 NDS) and demonstrates robustness to sensor corruptions and environmental changes.
By Yuting Zhao, Ziyi Zheng, Shuxiao Li
The paper introduces Post Fusion Stabilizer (PFS), a lightweight module that refines intermediate bird’s‑eye view (BEV) feature maps in existing camera‑LiDAR fusion detectors. PFS stabilizes feature statistics under domain shift, suppresses regions affected by sensor degradation, and adaptively restores weakened cues via residual correction, acting as a near‑identity transformation. On the nuScenes benchmark, PFS achieves state‑of‑the‑art robustness, notably improving camera dropout robustness by +1.2% and low‑light performance by +4.4% mAP while adding only 3.3 M parameters.
By Trung Tien Dong, Dev Thakkar, Arman Sargolzaei, Xiaomin Lin
TriSAR-3D is a new trimodal dataset combining high‑resolution LiDAR, RGB, and thermal imagery to capture people in varied indoor and outdoor poses, including lying and partially occluded positions. The authors propose PaintFusion, a lightweight trimodal 3D detection pipeline that fuses PointPainting evidence with an MLP, achieving 65.4 AP@50 and outperforming LiDAR‑only, RGB+LiDAR, and other trimodal baselines while running faster (51.6 ms per frame). Experiments show that removing lying instances from training drastically reduces detection performance, highlighting the importance of dataset diversity for robust 3D person detection in search and rescue scenarios.
By Markus Kramer, Jan Wachenfeld, Oskar von Stryk
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
DXPR is a depth‑based cross‑modal place recognition framework that matches monocular camera queries to a LiDAR map using a single vision foundation model backbone. By converting both modalities into a unified depth image representation, DXPR learns modality‑invariant global descriptors without modality‑specific encoders. A geometry‑aware overlap miner refines pairwise metric learning by computing pixel‑level overlap scores, and extensive tests on KITTI and Boreas show strong performance across seasons, weather, and day/night conditions, outperforming prior CMPR baselines.
arXiv:2605. 20301v2 Announce Type: replace-cross Abstract: In autonomous driving, 3D object detection is essential for accurate perception and reliable decision-making.
By Wenxuan Li, Qin Zou, Shoubing Chen, Chi Chen, Yingyi Yang, Qingxiang Meng