Cross-modal verification for 3D object detection
Haodi Zhang, Alexandrina Rogozan, Abdelaziz Bensrhair · ESANN 2021 proceedings · 2021
To overcome the deficiency in the single modality of LiDAR point cloud, we propose a cross-modal verification (CMV) model for reducing 3D object detection false positives.The abundant color and texture information in image modality allow the classification of the projection region of 3D bounding box proposal in the image plane.Three 3D object detectors are adopted as backbone and eight evaluation metrics are used to fully investigate the proposed model.The experiment results show that the proposed CMV model removes more than 50% of false positives in 3D object detection proposals and significantly improves the performance of 3D object detection. * This work