# Delving into the Pre-training Paradigm of Monocular 3D Object Detection Source: [Delving into the Pre-training Paradigm of Monocular 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:18:38.162Z Authors: Zhuoling Li, Chuanrui Zhang, En Yu, Haoqian Wang Published: 2022-06-08T03:01:13.000Z ## Abstract The labels of monocular 3D object detection (M3OD) are expensive to obtain. Meanwhile, there usually exists numerous unlabeled data in practical applications, and pre-training is an efficient way of exploiting the knowledge in unlabeled data. However, the pre-training paradigm for M3OD is hardly studied. We aim to bridge this gap in this work. To this end, we first draw two observations: (1) The guideline of devising pre-training tasks is imitating the representation of the target task. (2) Combining depth estimation and 2D object detection is a promising M3OD pre-training baseline. Afterwards, following the guideline, we propose several strategies to further improve this baseline, which mainly include target guided semi-dense depth estimation, keypoint-aware 2D object detection, and class-level loss adjustment. Combining all the developed techniques, the obtained pre-training framework produces pre-trained backbones that improve M3OD performance significantly on both the KITTI-3D and nuScenes benchmarks. For example, by applying a DLA34 backbone to a naive center-based M3OD detector, the moderate ${\\rm AP}\_{3D}70$ score of Car on the KITTI-3D testing set is boosted by 18.71\\% and the NDS score on the nuScenes validation set is improved by 40.41\\% relatively. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()