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Manhattan world: orientation and outlier detection by Bayesian inference
1Smith-Kettlewell Eye Research Institute, San Francisco, CA 94115, U.S.A. coughlan@ski.org
Neural Computation
|June 14, 2003
Summary
Many visual scenes utilize a "Manhattan" 3D grid structure, influencing image statistics. A Bayesian model accurately estimates viewer orientation and detects outliers within this Manhattan world framework.
Area of Science:
- Computer Vision
- Computational Imaging
- 3D Scene Understanding
Background:
- Visual scenes often exhibit regularities in their structure.
- Understanding 3D scene geometry is crucial for image interpretation.
- Previous models may not fully capture inherent scene regularities.
Purpose of the Study:
- To propose and validate a Bayesian model based on the "Manhattan world" assumption.
- To estimate viewer orientation relative to a 3D grid structure.
- To identify outlier structures deviating from the assumed grid.
Main Methods:
- Constructed a Bayesian model incorporating the Manhattan world assumption.
- Estimated viewer orientation using the developed Bayesian model.
- Implemented a null hypothesis model and log-likelihood ratio test to assess model applicability.
- Compared model-based orientation estimates with manual estimations.
Main Results:
- The Bayesian model accurately estimates viewer orientation for images conforming to the Manhattan world assumption.
- The model effectively detects structures misaligned with the grid.
- The Manhattan world assumption significantly improves orientation estimation accuracy.
Conclusions:
- The Manhattan world model provides a robust framework for analyzing visual scenes with grid-like structures.
- Viewer orientation estimation is significantly enhanced by assuming a Manhattan world.
- This approach offers a reliable method for identifying scene outliers and understanding 3D geometry.
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