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In this research, I proposed a network structure for multi-view 3D objectdetection using camera-only data and a Bird's-Eye-View map. My work is based ona current key challenge domain adaptation and visual data transfer. Althoughmany excellent camera-only 3D object detection has been continuously proposed,many research work risk dramatic performance drop when the networks are trainedon the source domain but tested on a different target domain. Then I found itis very surprising that predictions on bounding boxes and classes are stillreplied to on 2D networks. Based on the domain gap assumption on various 3Ddatasets, I found they still shared a similar data extraction on the same BEVmap size and camera data transfer. Therefore, to analyze the domain gapinfluence on the current method and to make good use of 3D space informationamong the dataset and the real world, I proposed a transfer learning method andTransformer construction to study the 3D object detection on NuScenes-mini andLyft. Through multi-dataset training and a detection head from the Transformer,the network demonstrated good data migration performance and efficientdetection performance by using 3D anchor query and 3D positional information.Relying on only a small amount of source data and the existing large modelpre-training weights, the efficient network manages to achieve competitiveresults on the new target domain. Moreover, my study utilizes 3D information asavailable semantic information and 2D multi-view image features blending intothe visual-language transfer design. In the final 3D anchor box prediction andobject classification, my network achieved good results on standard metrics of3D object detection, which differs from dataset-specific models on eachtraining domain without any fine-tuning.

M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object Detection