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

S. Sirko-Galouchenko, A. Boulch, S. Gidaris, A. Bursuc, A. Vobecky, P. Pérez, R. Marlet

Published in CVPR Workshop on Autonomous Driving (WAD), 2024

Arxiv

OccFeat teaser

Abstract

We introduce a self-supervised pretraining method, called OcFeat, 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.

Citation

@article{sirko2024occfeat,
  title={OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks},
  author={Sirko-Galouchenko, Sophia and Boulch, Alexandre and Gidaris, Spyros and Bursuc, Andrei and Vobecky, Antonin and P{\'e}rez, Patrick and Marlet, Renaud},
  journal={arXiv preprint arXiv:2404.14027},
  year={2024}
}