OmniOVCD: Streamlining Open-Vocabulary Change Detection with SAM 3

• URL: http://arxiv.org/abs/2601.13895v1
• Date: Tue, 20 Jan 2026 12:25:41 GMT
• Title: OmniOVCD: Streamlining Open-Vocabulary Change Detection with SAM 3
• Abstract summary: Open-Vocabulary Change Detection (OVCD) aims to reduce the reliance on predefined categories.<n>In this paper, we propose OmniOVCD, a standalone framework designed for OVCD.<n>Experiments on four public benchmarks demonstrate SOTA performance, achieving IoU scores of 67.2, 66.5, 24.5, and 27.1 (class-average)
• Score: 19.94566126701934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change Detection (CD) is a fundamental task in remote sensing. It monitors the evolution of land cover over time. Based on this, Open-Vocabulary Change Detection (OVCD) introduces a new requirement. It aims to reduce the reliance on predefined categories. Existing training-free OVCD methods mostly use CLIP to identify categories. These methods also need extra models like DINO to extract features. However, combining different models often causes problems in matching features and makes the system unstable. Recently, the Segment Anything Model 3 (SAM 3) is introduced. It integrates segmentation and identification capabilities within one promptable model, which offers new possibilities for the OVCD task. In this paper, we propose OmniOVCD, a standalone framework designed for OVCD. By leveraging the decoupled output heads of SAM 3, we propose a Synergistic Fusion to Instance Decoupling (SFID) strategy. SFID first fuses the semantic, instance, and presence outputs of SAM 3 to construct land-cover masks, and then decomposes them into individual instance masks for change comparison. This design preserves high accuracy in category recognition and maintains instance-level consistency across images. As a result, the model can generate accurate change masks. Experiments on four public benchmarks (LEVIR-CD, WHU-CD, S2Looking, and SECOND) demonstrate SOTA performance, achieving IoU scores of 67.2, 66.5, 24.5, and 27.1 (class-average), respectively, surpassing all previous methods.

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