  Multi-view segmentation in Remote Sensing (RS) seeks to segment images from
diverse perspectives within a scene. Recent methods leverage 3D information
extracted from an Implicit Neural Field (INF), bolstering result consistency
across multiple views while using limited accounts of labels (even within 3-5
labels) to streamline labor. Nonetheless, achieving superior performance within
the constraints of limited-view labels remains challenging due to inadequate
scene-wide supervision and insufficient semantic features within the INF. To
address these. we propose to inject the prior of the visual foundation
model-Segment Anything(SAM), to the INF to obtain better results under the
limited number of training data. Specifically, we contrast SAM features between
testing and training views to derive pseudo labels for each testing view,
augmenting scene-wide labeling information. Subsequently, we introduce SAM
features via a transformer into the INF of the scene, supplementing the
semantic information. The experimental results demonstrate that our method
outperforms the mainstream method, confirming the efficacy of SAM as a
supplement to the INF for this task.
