COARSE: Collaborative Pseudo-Labeling with Coarse Real Labels for Off-Road Semantic Segmentation
This addresses the challenge of robust perception for autonomous off-road navigation, though it appears incremental as it builds on existing semi-supervised and domain adaptation methods.
The paper tackles the problem of scarce labeled data for off-road semantic segmentation by proposing COARSE, a semi-supervised domain adaptation framework that leverages sparse coarse labels and dense out-of-domain data, achieving improvements of 9.7% and 8.4% on RUGD and Rellis-3D datasets.
Autonomous off-road navigation faces challenges due to diverse, unstructured environments, requiring robust perception with both geometric and semantic understanding. However, scarce densely labeled semantic data limits generalization across domains. Simulated data helps, but introduces domain adaptation issues. We propose COARSE, a semi-supervised domain adaptation framework for off-road semantic segmentation, leveraging sparse, coarse in-domain labels and densely labeled out-of-domain data. Using pretrained vision transformers, we bridge domain gaps with complementary pixel-level and patch-level decoders, enhanced by a collaborative pseudo-labeling strategy on unlabeled data. Evaluations on RUGD and Rellis-3D datasets show significant improvements of 9.7\% and 8.4\% respectively, versus only using coarse data. Tests on real-world off-road vehicle data in a multi-biome setting further demonstrate COARSE's applicability.