OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks

Sophia Sirko-Galouchenko, Alexandre Boulch, Spyros Gidaris, Andrei Bursuc, Antonin Vobecky, Patrick Pérez, Renaud Marlet; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4493-4503

We introduce a self-supervised pretraining method called OccFeat for camera-only Bird's-Eye-View (BEV) segmentation networks. With OccFeat we pretrain a BEV network via occupancy prediction and feature distillation tasks. Occupancy prediction provides a 3D geometric understanding of the scene to the model. However the geometry learned is class-agnostic. Hence we add semantic information to the model in the 3D space through distillation from a self-supervised pretrained image foundation model. Models pretrained with our method exhibit improved BEV semantic segmentation performance particularly in low-data scenarios. Moreover empirical results affirm the efficacy of integrating feature distillation with 3D occupancy prediction in our pretraining approach.