无需标签，多视角融合实现精准变化定位。
Multi-View Pose-Agnostic Change Localization with Zero Labels
- 基于多视角信息构建3D高斯点云，自动学习变化通道。
- 仅需5张图像即可生成优于单视角的掩码，性能提升1.7倍和1.5倍。
- 适用于新视角生成，适合复杂场景变化检测任务。
自主代理常需在非约束、视角不一致的情况下准确检测并定位环境变化。本文提出一种无标签、视角无关的变化检测方法，通过融合多视角信息构建场景的3D高斯点云（3DGS）表示，并引入额外的变化通道。仅需5张变更后场景图像，该方法即可学习变化通道并生成高质量变化掩码，表现优于单视角方法。所提出的感知变化3D场景表示还能为未见视角生成准确掩码。实验表明，在复杂多物体场景中达到当前最优性能，均交并比（mIoU）与F1分数分别较基线提升1.7倍和1.5倍。同时，我们还发布了新的真实世界数据集，用于在光照变化等挑战下评估变化检测性能。
原文摘要 · Abstract (English)
Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information from multiple viewpoints to construct a change-aware 3D Gaussian Splatting (3DGS) representation of the scene. With as few as 5 images of the post-change scene, our approach can learn an additional change channel in a 3DGS and produce change masks that outperform single-view techniques. Our change-aware 3D scene representation additionally enables the generation of accurate change masks for unseen viewpoints. Experimental results demonstrate state-of-the-art performance in complex multi-object scenes, achieving a 1.7x and 1.5x improvement in Mean Intersection Over Union and F1 score respectively over other baselines. We also contribute a new real-world dataset to benchmark change detection in diverse challenging scenes in the presence of lighting variations.
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。