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。

基于 SAM 先验的多视角遥感图像分割

Multi-view Remote Sensing Image Segmentation With SAM priors

Traditional Radiance Field (RF) representations capture details of a specific
scene and must be trained afresh on each scene. Semantic feature fields have
been added to RFs to facilitate several segmentation tasks. Generalised RF
representations learn the principles of view interpolation. A generalised RF
can render new views of an unknown and untrained scene, given a few views. We
present a way to distil feature fields into the generalised GNT representation.
Our GSN representation generates new views of unseen scenes on the fly along
with consistent, per-pixel semantic features. This enables multi-view
segmentation of arbitrary new scenes. We show different semantic features being
distilled into generalised RFs. Our multi-view segmentation results are on par
with methods that use traditional RFs. GSN closes the gap between standard and
generalisable RF methods significantly. Project Page:
this https URL

将语义特征场转化为广义 GNT 表示，实现对未知场景的实时多视角分割。

GSN: 神经辐射场的通用分割

GSN: Generalisable Segmentation in Neural Radiance Field

This paper presents an approach for reconstruction of 4D temporally coherent
models of complex dynamic scenes. No prior knowledge is required of scene
structure or camera calibration allowing reconstruction from multiple moving
cameras. Sparse-to-dense temporal correspondence is integrated with joint
multi-view segmentation and reconstruction to obtain a complete 4D
representation of static and dynamic objects. Temporal coherence is exploited
to overcome visual ambiguities resulting in improved reconstruction of complex
scenes. Robust joint segmentation and reconstruction of dynamic objects is
achieved by introducing a geodesic star convexity constraint. Comparative
evaluation is performed on a variety of unstructured indoor and outdoor dynamic
scenes with hand-held cameras and multiple people. This demonstrates
reconstruction of complete temporally coherent 4D scene models with improved
nonrigid object segmentation and shape reconstruction.

本文介绍了一种重建 4D 时间连续场景模型的方法，该方法使用多个移动相机进行重建，可以重建动态或静态物体，同时将稀疏 - 稠密时序对应与联合多视角分割和重建结合使用，利用时间上的连续性克服了视觉歧义，并通过引入测地线星凸性的约束实现了具有鲁棒性的动态物体分割和重建。