SceneDiff: A Benchmark and Method for Multiview Object Change Detection

• URL: http://arxiv.org/abs/2512.16908v1
• Date: Thu, 18 Dec 2025 18:59:02 GMT
• Title: SceneDiff: A Benchmark and Method for Multiview Object Change Detection
• Authors: Yuqun Wu, Chih-hao Lin, Henry Che, Aditi Tiwari, Chuhang Zou, Shenlong Wang, Derek Hoiem,
• Abstract summary: We introduce SceneDiff Benchmark, the first multiview change detection benchmark with object instance annotations.<n>We also introduce SceneDiff, a new training-free approach for multiview object change detection.<n>Our method aligns the captures in 3D, extracts object regions, and compares spatial and semantic region features to detect changes.
• Score: 24.67954935241515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the problem of identifying objects that have been added, removed, or moved between a pair of captures (images or videos) of the same scene at different times. Detecting such changes is important for many applications, such as robotic tidying or construction progress and safety monitoring. A major challenge is that varying viewpoints can cause objects to falsely appear changed. We introduce SceneDiff Benchmark, the first multiview change detection benchmark with object instance annotations, comprising 350 diverse video pairs with thousands of changed objects. We also introduce the SceneDiff method, a new training-free approach for multiview object change detection that leverages pretrained 3D, segmentation, and image encoding models to robustly predict across multiple benchmarks. Our method aligns the captures in 3D, extracts object regions, and compares spatial and semantic region features to detect changes. Experiments on multi-view and two-view benchmarks demonstrate that our method outperforms existing approaches by large margins (94% and 37.4% relative AP improvements). The benchmark and code will be publicly released.

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