Changes in Real Time: Online Scene Change Detection with Multi-View Fusion
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908849414340608 |
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| author | Galappaththige, Chamuditha Jayanga Lai, Jason Windrim, Lloyd Dansereau, Donald Sünderhauf, Niko Miller, Dimity |
| author_facet | Galappaththige, Chamuditha Jayanga Lai, Jason Windrim, Lloyd Dansereau, Donald Sünderhauf, Niko Miller, Dimity |
| contents | Online Scene Change Detection (SCD) is an extremely challenging problem that requires an agent to detect relevant changes on the fly while observing the scene from unconstrained viewpoints. Existing online SCD methods are significantly less accurate than offline approaches. We present the first online SCD approach that is pose-agnostic, label-free, and ensures multi-view consistency, while operating at over 10 FPS and achieving new state-of-the-art performance, surpassing even the best offline approaches. Our method introduces a new self-supervised fusion loss to infer scene changes from multiple cues and observations, PnP-based fast pose estimation against the reference scene, and a fast change-guided update strategy for the 3D Gaussian Splatting scene representation. Extensive experiments on complex real-world datasets demonstrate that our approach outperforms both online and offline baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Changes in Real Time: Online Scene Change Detection with Multi-View Fusion Galappaththige, Chamuditha Jayanga Lai, Jason Windrim, Lloyd Dansereau, Donald Sünderhauf, Niko Miller, Dimity Computer Vision and Pattern Recognition Online Scene Change Detection (SCD) is an extremely challenging problem that requires an agent to detect relevant changes on the fly while observing the scene from unconstrained viewpoints. Existing online SCD methods are significantly less accurate than offline approaches. We present the first online SCD approach that is pose-agnostic, label-free, and ensures multi-view consistency, while operating at over 10 FPS and achieving new state-of-the-art performance, surpassing even the best offline approaches. Our method introduces a new self-supervised fusion loss to infer scene changes from multiple cues and observations, PnP-based fast pose estimation against the reference scene, and a fast change-guided update strategy for the 3D Gaussian Splatting scene representation. Extensive experiments on complex real-world datasets demonstrate that our approach outperforms both online and offline baselines. |
| title | Changes in Real Time: Online Scene Change Detection with Multi-View Fusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.12370 |