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Robust methods for geometric primitive recovery and estimation from range images.

Irina Lavva¹, Eyal Hameiri, Ilan Shimshoni

¹Department of Computer Science, The Technion-Israel Institute of Technology, Haifa 32000, Israel. lavva@cs.technion.ac.il

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|June 19, 2008

Summary

This study introduces a novel method for recovering 3-D geometric primitives from range images, even with occlusions and clutter. The technique efficiently combines segmentation, classification, and fitting for robust shape recovery.

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Area of Science:

Computer Vision
Geometric Modeling
Robotics

Background:

Recovering 3-D geometric primitives from range data is crucial for scene understanding.
Partially occluded objects and non-primitive shapes present significant challenges in existing methods.

Purpose of the Study:

To develop a robust and efficient method for recovering partially occluded 3-D geometric primitives from range images.
To integrate segmentation, classification, and fitting into a unified process.

Main Methods:

Estimating principal curvatures and Darboux frames from range images.
Utilizing mean-shift and random-sample-consensus algorithms for robust parameter and model estimation.
Employing a minimum-description-length method for selecting the best descriptive subset of recovered models.

Main Results:

Accurate and robust recovery of geometric primitives (planes, spheres, cylinders, cones, tori) from complex, cluttered real-world scenes.
Efficiently combines multiple recovery steps into a single, monotonous process.
Demonstrated effectiveness on challenging datasets with occlusions and non-primitive objects.

Conclusions:

The proposed method offers a significant advancement in the automated recovery of 3-D geometric primitives.
The integration of robust statistical techniques enhances accuracy and resilience in cluttered environments.
This approach provides a powerful tool for 3-D scene analysis and reconstruction.