Detect 3D object with many cameras using perspective debiasing Lu
Detect 3D object with many cameras using perspective debiasing Lu
Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing
arXiv paper abstract https://arxiv.org/abs/2310.11346
arXiv PDF paper https://arxiv.org/pdf/2310.11346.pdf
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.
... propose a novel method that aligns 3D detection with 2D camera plane results, ensuring consistent and accurate detections.
... framework, anchored in perspective debiasing, helps the learning of features resilient to domain shifts.
... 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.
... show ... approach achieves satisfactory results in real data when trained only with virtual datasets, eliminating the need for real scene annotations.
Experimental results ... clearly demonstrate its effectiveness ...
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