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arXiv Computer Vision By Yun Zhang, Zhaoliang Zheng, Johnson Liu, Zhiyu Huang, Zewei Zhou, Zonglin Meng, Tianhui Cai, Jiaqi Ma

MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection

Read the original on arXiv Computer Vision →

MIC-BEV is a Transformer-based bird's-eye-view perception framework designed for infrastructure-based multi-camera 3D object detection. It supports a variable number of heterogeneous cameras and employs a graph-enhanced fusion module to adaptively integrate multi-view image features by modeling geometric relationships between cameras and the BEV grid. The authors introduce the M2I synthetic dataset and evaluate MIC-BEV on both M2I and the real-world RoScenes dataset, demonstrating strong detection performance, improved robustness under challenging conditions, and practical feasibility for deployment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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