Learning from Automatic 3D Gaussian Splatting Edits for Online Bayesian Scene Change Detection
Online scene change detection requires integrating incoming observations to determine which regions have changed relative to a reference scene.
ProofPaper ↗
Key points
- We update change beliefs as new images arrive using generalised Bayesian evidence fusion.
- We address these challenges by learning offline from automatic edits to a reference 3D Gaussian Splatting (3DGS) model.
- Together with unchanged reference observations, they provide supervision for learning scene-specific evidence models and estimating the effective pixel prior and response statistics.
- Online prediction combines current evidence with historical support, while closed-form local Bayes factors, parameterized by the learned response statistics, update an accumulated 3D Gaussian field.
Sources (1)
- [1]Learning from Automatic 3D Gaussian Splatting Edits for Online Bayesian Scene Change DetectionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:36 PM
Online scene change detection requires integrating incoming observations to determine which regions have changed relative to a reference scene.
We update change beliefs as new images arrive using generalised Bayesian evidence fusion.
Extractive summary: sentences quoted from the sources.
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