View-to-Label: Multi-View Consistency for Self-Supervised 3D Object
  Detection
Issa Mouawad Nikolas Brasch Fabian Manhardt Federico Tombari Francesca Odone
For autonomous vehicles, driving safely is highly dependent on the capability
to correctly perceive the environment in 3D space, hence the task of 3D object
detection represents a fundamental aspect of perception. While 3D sensors
deliver accurate metric perception, monocular approaches enjoy cost and
availability advantages that are valuable in a wide range of applications.
Unfortunately, training monocular methods requires a vast amount of annotated
data. Interestingly, self-supervised approaches have recently been successfully
applied to ease the training process and unlock access to widely available
unlabelled data. While related research leverages different priors including
LIDAR scans and stereo images, such priors again limit usability. Therefore, in
this work, we propose a novel approach to self-supervise 3D object detection
purely from RGB sequences alone, leveraging multi-view constraints and weak
labels. Our experiments on KITTI 3D dataset demonstrate performance on par with
state-of-the-art self-supervised methods using LIDAR scans or stereo images.