3D Scene Change Modeling With Consistent Multi-View Aggregation

• URL: http://arxiv.org/abs/2512.22830v1
• Date: Sun, 28 Dec 2025 08:00:56 GMT
• Title: 3D Scene Change Modeling With Consistent Multi-View Aggregation
• Authors: Zirui Zhou, Junfeng Ni, Shujie Zhang, Yixin Chen, Siyuan Huang,
• Abstract summary: SCaR-3D is 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.<n>Our approach consists of a signed-distance-based 2D differencing module followed by multi-view aggregation with voting and pruning.<n>We also develop a continual scene reconstruction strategy that selectively updates dynamic regions while preserving the unchanged areas.
• Score: 18.547603626073585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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