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• Prior2DSM achieves up to a 46% reduction in RMSE compared to linear fitting of monocular depth estimation (MDE) models for DSM completion.result 95%
• Prior2DSM is a training-free framework that operates entirely at test time, unlike supervised learning-based approaches.method 90%
• The method combines self-supervised Vision Transformer (ViT) features from DINOv3 with monocular depth foundation models for height completion.technical 90%
• Traditional height completion approaches fail when objects are missing because they assume spatial continuity.limitation 85%
• The method uses test-time adaptation (TTA) with parameter-efficient low-rank adaptation (LoRA) and a lightweight MLP.technical 90%
• Prior2DSM enables DSM updating and coupled RGB-DSM generation.technical 85%
• The method propagates metric information from incomplete height priors through semantic feature-space correspondence.method 85%
• Supervised learning-based approaches for height completion typically require sensor-specific datasets, limiting generalization across domains.limitation 80%

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