Our paper by lead author Andrej Janda on self-supervised pre-training for 3D semantic segmentation won the Dyson Best Paper Award at the CoRL’22 Pre-Training Robot Learning Workshop! The paper describes an approach for pre-training 3D segmentation networks using 2D image data; our contrastive learning approach enables self-supervision and 2D-to-3D feature transfer. Better segmentation with far less (or no) manual labelling!

Congratulations to Andrej, Brandon, and Edwin! The short paper is available on arXiv: https://arxiv.org/abs/2211.11801. A big thank you to the workshop organizers and to Dyson for sponsoring the workshop award!