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

arXiv:2609.29235 · cs.CV, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Robustness — 15× in this paper · explained in 5 episodes
• Transformer — 14× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Point Clouds — 5× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Computer Vision and Pattern Recognition — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• On the Robustness of 3D Object Detectors
• TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
• MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection
• Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
• M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
• nuScenes: A multimodal dataset for autonomous driving
• Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble
• FUTR3D: A Unified Sensor Fusion Framework for 3D Detection
• AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection
• Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving
• DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars
• MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation
• BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View
• Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective
• PRM-as-a-Judge: A Dense Evaluation Paradigm for Fine-Grained Robotic Auditing
• MathSticks: A Benchmark for Visual Symbolic Compositional Reasoning with Matchstick Puzzles
• VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning
• Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
• BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
• BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models