Summary of Occfeat: Self-supervised Occupancy Feature Prediction For Pretraining Bev Segmentation Networks, by Sophia Sirko-galouchenko et al.
OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
by Sophia Sirko-Galouchenko, Alexandre Boulch, Spyros Gidaris, Andrei Bursuc, Antonin Vobecky, Patrick Pérez, Renaud Marlet
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We introduce OccFeat, a self-supervised pretraining method for camera-only Bird’s-Eye-View (BEV) segmentation networks. The approach combines occupancy prediction and feature distillation tasks to pretrain BEV networks. Occupancy prediction provides 3D geometric understanding, while feature distillation integrates semantic information from a self-supervised pretrained image foundation model. Models trained with OccFeat exhibit improved BEV semantic segmentation performance, especially in low-data scenarios. Our empirical results affirm the effectiveness of integrating feature distillation with 3D occupancy prediction. The proposed method is particularly relevant for applications that require accurate BEV segmentation, such as autonomous driving and robotics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We developed a new way to train computers to understand 2D images taken from directly above (Bird’s-Eye-View). Our approach uses a combination of two tasks: predicting the occupancy (or presence) of objects in the scene and distilling features from an already-trained image foundation model. This pretraining method, called OccFeat, helps BEV segmentation networks learn to better recognize objects even with limited data. The results show that our method improves the performance of BEV semantic segmentation, which is important for applications like self-driving cars and robots. |
Keywords
» Artificial intelligence » Distillation » Pretraining » Self supervised » Semantic segmentation
