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

提出了一种使用相机数据和鸟瞰图的多视角三维物体检测的网络结构，通过域适应和可视数据传输解决了当前的关键挑战。