Self-Supervised 3D Occupancy Prediction
Self-supervised 3D occupancy prediction for self-driving using SRT-based volumetric rendering.
Improved 3D occupancy prediction for self-driving by training with depth priors such as LiDAR and removing dynamic objects, using an SRT-based volumetric rendering approach for self-supervised learning.
At: TUM Computer Vision Group, Munich