Detecting objects in 3D space using multiple cameras, known as Multi-Camera
3D Object Detection (MC3D-Det), has gained prominence with the advent of
bird's-eye view (BEV) approaches. However, these methods often struggle when
faced with unfamiliar testing environments due to the lack of diverse training
data encompassing various viewpoints and environments. To address this, we
propose a novel method that aligns 3D detection with 2D camera plane results,
ensuring consistent and accurate detections. Our framework, anchored in
perspective debiasing, helps the learning of features resilient to domain
shifts. In our approach, we render diverse view maps from BEV features and
rectify the perspective bias of these maps, leveraging implicit foreground
volumes to bridge the camera and BEV planes. This two-step process promotes the
learning of perspective- and context-independent features, crucial for accurate
object detection across varying viewpoints, camera parameters and environment
conditions. Notably, our model-agnostic approach preserves the original network
structure without incurring additional inference costs, facilitating seamless
integration across various models and simplifying deployment. Furthermore, we
also show our approach achieves satisfactory results in real data when trained
only with virtual datasets, eliminating the need for real scene annotations.
Experimental results on both Domain Generalization (DG) and Unsupervised Domain
Adaptation (UDA) clearly demonstrate its effectiveness. Our code will be
released.

通过与 2D 相机平面结果对齐的方法，本研究提出了一种新颖的多相机三维目标检测方法，确保持一致且准确的检测结果。该方法通过视角校正，将鸟瞰视图特征渲染为多视图地图，并纠正了这些地图的透视偏差，利用隐含的前景体来连接相机与鸟瞰视图平面。该两步过程促进了视角和上下文无关特征的学习，对于准确地检测不同视角、相机参数和环境条件下的目标至关重要。此外，该方法采用基于模型的不可知的方法，而不需要额外的推理成本和对真实场景注释，从而促进了各种模型的无缝集成和简化部署。实验结果在领域泛化和无监督领域自适应方面明确展示了其有效性。