3D Data Augmentation for Driving Scenes on Camera

Understand

Driving scenes are extremely diverse and complicated that it is impossible to collect all cases with human effort alone.

• While data augmentation is an effective technique to enrich the training data, existing methods for camera data in autonomous driving applications are confined to the 2D image plane, which may not optimally increase data diversity in 3D real-world scenarios.
• To this end, we propose a 3D data augmentation approach termed Drive-3DAug, aiming at augmenting the driving scenes on camera in the 3D space.
• We first utilize Neural Radiance Field (NeRF) to reconstruct the 3D models of background and foreground objects.