  Unsupervised Domain Adaptation has been an efficient approach to transferring
the semantic segmentation model across data distributions. Meanwhile, the
recent Open-vocabulary Semantic Scene understanding based on large-scale vision
language models is effective in open-set settings because it can learn diverse
concepts and categories. However, these prior methods fail to generalize across
different camera views due to the lack of cross-view geometric modeling. At
present, there are limited studies analyzing cross-view learning. To address
this problem, we introduce a novel Unsupervised Cross-view Adaptation Learning
approach to modeling the geometric structural change across views in Semantic
Scene Understanding. First, we introduce a novel Cross-view Geometric
Constraint on Unpaired Data to model structural changes in images and
segmentation masks across cameras. Second, we present a new Geodesic Flow-based
Correlation Metric to efficiently measure the geometric structural changes
across camera views. Third, we introduce a novel view-condition prompting
mechanism to enhance the view-information modeling of the open-vocabulary
segmentation network in cross-view adaptation learning. The experiments on
different cross-view adaptation benchmarks have shown the effectiveness of our
approach in cross-view modeling, demonstrating that we achieve State-of-the-Art
(SOTA) performance compared to prior unsupervised domain adaptation and
open-vocabulary semantic segmentation methods.
