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.

通过将可视化基础模型 Segment Anything（SAM）注入到隐式神经场模型 - INF 中，我们提出了一种新的多视图遥感图像分割方法，通过对测试视图和训练视图之间的 SAM 特征进行对比，得出每个测试视图的伪标签，从而增强整个场景的标注信息，实验证明我们的方法在有限的训练数据情况下优于主流方法，从而证实了 SAM 作为 INF 的一种补充在这一任务中的有效性。