Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation

Understand

The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data.

• Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views due to the lack of considering 3D information within the scene.
• In this paper, we propose ''Implicit Ray-Transformer (IRT)'' based on Implicit Neural Representation (INR), for RS scene semantic segmentation with sparse labels (such as 4-6 labels per 100 images).
• We explore a new way of introducing multi-view 3D structure priors to the task for accurate and view-consistent semantic segmentation.