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Geometric Unsupervised Domain Adaptation for Semantic Segmentation

2021-03-30 21:33:00
Vitor Guizilini, Jie Li, Rares Ambrus, Adrien Gaidon

Abstract

tract: Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation. However, they introduce a domain gap that severely hurts real-world performance. We propose to use self-supervised monocular depth estimation as a proxy task to bridge this gap and improve sim-to-real unsupervised domain adaptation (UDA). Our Geometric Unsupervised Domain Adaptation method (GUDA) learns a domain-invariant representation via a multi-task objective combining synthetic semantic supervision with real-world geometric constraints on videos. GUDA establishes a new state of the art in UDA for semantic segmentation on three benchmarks, outperforming methods that use domain adversarial learning, self-training, or other self-supervised proxy tasks. Furthermore, we show that our method scales well with the quality and quantity of synthetic data while also improving depth prediction.

Abstract (translated)

URL

https://arxiv.org/abs/2103.16694

PDF

https://arxiv.org/pdf/2103.16694


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