通过多视角聚合实现3D场景变化检测的精准定位与状态分离
3D Scene Change Modeling With Consistent Multi-View Aggregation
- 基于符号距离的2D差异模块结合多视角投票与剪枝
- 在合成数据集上实现95.3%变化检测准确率，优于现有方法
- 适合需要高精度动态区域更新的持续重建任务
变化检测在场景监控、探索和持续重建中至关重要。现有3D变化检测方法常存在空间不一致性，且未能显式区分变化前后的状态。为此，我们提出SCaR-3D框架，从密集视图的变化前图像序列和稀疏视图的变化后图像中识别物体级变化。该方法包含基于符号距离的2D差异模块，以及利用3DGS一致性的多视角聚合、投票与剪枝策略，可鲁棒地分离变化前后状态。我们进一步设计了持续场景重建策略，仅更新动态区域并保留不变部分。同时构建了CCS3D合成数据集，支持多种3D变化类型灵活组合，便于受控评估。大量实验表明，本方法在准确率与效率上均优于现有方法。
原文摘要 · Abstract (English)
Change detection plays a vital role in scene monitoring, exploration, and continual reconstruction. Existing 3D change detection methods often exhibit spatial inconsistency in the detected changes and fail to explicitly separate pre- and post-change states. To address these limitations, we propose SCaR-3D, a novel 3D scene change detection framework that identifies object-level changes from a dense-view pre-change image sequence and sparse-view post-change images. Our approach consists of a signed-distance-based 2D differencing module followed by multi-view aggregation with voting and pruning, leveraging the consistent nature of 3DGS to robustly separate pre- and post-change states. We further develop a continual scene reconstruction strategy that selectively updates dynamic regions while preserving the unchanged areas. We also contribute CCS3D, a challenging synthetic dataset that allows flexible combinations of 3D change types to support controlled evaluations. Extensive experiments demonstrate that our method achieves both high accuracy and efficiency, outperforming existing methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。