Robust surface reconstruction based on local estimation and refinement: 1-D results

Charles V. Stewart

doi:10.1117/12.57953

30 April 1992 Robust surface reconstruction based on local estimation and refinement: 1-D results

Charles V. Stewart

Proceedings Volume 1611, Sensor Fusion IV: Control Paradigms and Data Structures; (1992) https://doi.org/10.1117/12.57953
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

The problem of surface reconstruction from sparse visual depth data is studied. Because of the difficulties caused by outliers in the depth data and by overlapping data points from multiple surfaces, the reconstruction problem is posed as a data clustering problem. This problem is approached using a two phase technique. The first phase is a new robust fitting algorithm that overcomes some of the limitations of the Least Median of Squares robust technique. The second phase is an efficient relaxation style algorithm to refine the linear segments provided by the first phase to make second order estimates of the surface, and cluster estimates whose positions, orientations and curvatures make them consistent. Preliminary experimental results on one-dimensional synthetic data demonstrate the promise of the approach.

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Charles V. Stewart "Robust surface reconstruction based on local estimation and refinement: 1-D results", Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); https://doi.org/10.1117/12.57953

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KEYWORDS

Image segmentation

Reconstruction algorithms

Sensor fusion

Data modeling

Image processing

3D image reconstruction

Image processing algorithms and systems

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