SRGCD：稳定性驱动的区域生长三维变化检测框架
SRGCD: Stability-Driven Region Growth Framework for 3D Change Detection
把 3D 变化检测从逐点二分类重构为从高置信种子出发的区域生长，用稳定性引导的受控注意力逐层传播，可直接迁移到点云分割中边界模糊与孤立噪声点的处理。
原文依据 we propose SRGCD, a Stability-Driven Region Growth Framework that redefines 3DCD as region growing rather than segmentation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论随着激光雷达与摄影测量技术使大规模三维点云的获取日益便捷，三维变化检测（3DCD）已成为理解动态场景的关键手段。现有方法通常将其建模为分割问题，对每个点独立进行二分类，这会在区域内部产生孤立的误分类噪声点；同时，边界处的特征相似性导致边界模糊。变化检测本身更严重的类别不平衡进一步加剧了这一问题。为应对上述挑战，本文提出 SRGCD，一种稳定性驱动的区域生长框架，将 3DCD 重新定义为区域生长而非分割。其核心洞见在于：从高置信种子出发逐步扩展，既能规避逐点分类的缺陷，又能优雅地缓解类别不平衡。具体而言，本文首先通过互几何一致性先验施加严格约束，以识别出极少量的高可靠未变化种子；随后，稳定性引导的受控注意力模块从这些种子出发，将稳定性从稳定区域逐步传播至邻近的不确定点，使未变化区域由内部核心向边界逐层生长。这一由粗到细的生长过程自然形成连贯区域，避免孤立噪声，并通过渐进扩展获得紧凑且边界清晰的区域。在合成数据集 Urb3DCD 与真实场景数据集 HKCD 上的大量实验表明，SRGCD 取得了最优性能，在内部完整性与边界紧凑性上显著优于现有方法。
英文原文摘要 1657 字符
With the growing accessibility of large-scale 3D point clouds from LiDAR and photogrammetric techniques, 3D change detection (3DCD) has become essential for understanding dynamic scenes. Existing methods typically formulate this as segmentation, treating each point independently for binary classification. This leads to isolated misclassified noise points inside regions. Meanwhile, feature similarity at boundaries causes boundary ambiguity. The more severe class imbalance inherent to change detection further exacerbates this issue. To address these challenges, we propose SRGCD, a Stability-Driven Region Growth Framework that redefines 3DCD as region growing rather than segmentation. Our key insight is that progressively expanding from highly confident seeds avoids pitfalls of point-wise classification while elegantly alleviating class imbalance. Specifically, we first apply strict constraints through Mutual Geometric Consistency Prior to identify minimal highly reliable unchanged seeds. From these seeds, Stability-Guided Controlled Attention modules progressively propagate stability from stable regions to neighboring uncertain points, enabling unchanged regions to grow layer-by-layer from interior cores toward boundaries. This coarse-to-fine growing process naturally forms coherent regions, avoiding isolated noise while achieving compact, well-defined boundaries through progressive expansion. Extensive experiments on the synthetic dataset Urb3DCD and the real-world dataset HKCD demonstrate that SRGCD achieves state-of-the-art performance, significantly improving interior completeness and boundary compactness over existing methods.