Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training

arXiv:2310.00920v2 Announce Type: replace
Abstract: Monocular 3D object detection plays a crucial role in autonomous driving. However, existing monocular 3D detection algorithms depend on 3D labels derived from LiDAR measurements, which are costly to acquire for new datasets and challenging to deploy in novel environments. Specifically, this study investigates the pipeline for training a monocular 3D object detection model on a diverse collection of 3D and 2D datasets. The proposed framework comprises three components: (1) a robust monocular 3D model …

3d object 3d object detection abstract algorithms arxiv autonomous autonomous driving cs.cv dataset datasets deploy detection driving environments every however labels lidar novel object role scaling scaling up training type