arXiv AI
Sep 25

SARFusion: Scene-Aware Routing Fusion for Robust Camera-LiDAR 3D Object Detection

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
arXiv Computer Vision
Oct 28

FastBEVFusion: Simple Multi-Modal Fusion for 3D Object Detection at 50 FPS

FastBEVFusion is a lightweight fusion system for real‑time 3D object detection that combines LiDAR and camera data without depth estimation. It projects modality‑specific features into semi‑aligned BEV spaces and fuses them with a dynamic cross‑attention module, achieving over 50 FPS on the nuScenes dataset. The method also demonstrates strong robustness to calibration errors.

By Tom Sanitz, Emre G\"ulsoylu, Simone Frintrop, Christian Wilms
arXiv Computer Vision
Oct 22

AutoExpert: Automating 3D LiDAR Annotation from Expert-Crafted Guidelines

AutoExpert proposes a new benchmark for automating 3D LiDAR annotation using expert‑crafted guidelines, redefining evaluation protocols and re‑annotating nuScenes and PandaSet datasets. The pipeline combines 2D detection and segmentation in RGB images, lifts these detections into 3D with known sensor poses, and generates 3D cuboids, improving 3D detection mAP from 12.1 to 25.4 on the AutoExpert‑nuScenes benchmark.

By Yechi Ma, Wei Hua, Shu Kong