Auto-Annotation of 3D Objects via ImageNet

Automatic annotation of 3D objects in cluttered scenes shows its great importance to a variety of applications. Nowadays, 3D point clouds, a new 3D representation of real-world objects, can be easily and rapidly collected by mobile LiDAR systems, e.g. RIEGL VMX-450 system. Moreover, the mobile LiDAR system can also provide a series of consecutive multi-view images which are calibrated with 3D point clouds. This paper proposes to automatically annotate 3D objects of interest in point clouds of road scenes by exploiting a multitude of annotated images in image databases, such as LabelMe and ImageNet. In the proposed method, an object detector trained on the annotated images is used to locate the object regions in acquired multi-view images. Then, based on the correspondences between multi-view images and 3D point clouds, a probabilistic graphical model is used to model the temporal, spatial and geometric constraints to extract the 3D objects automatically. A new dataset was built for evaluation and the experimental results demonstrate a satisfied performance on 3D object extraction.