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Efficient 3-d object representations for industrial vision systems.

T C Henderson¹

¹Department of Computer Science, University of Utah, Salt Lake City, UT 84112.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study presents efficient methods for creating 3-D object surface representations from intrinsic images. An algorithm using region growing and spatial proximity graphs effectively segments 3-D point sets into planar faces for industrial applications.

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Area of Science:

Computer Vision
3-D Reconstruction
Geometric Modeling

Background:

3-D object representation is crucial for scene analysis.
Surface-based techniques are highly effective for this representation.
Intrinsic images provide valuable data for surface reconstruction.

Purpose of the Study:

To explore and evaluate methods for obtaining 3-D object surface representations.
To investigate the efficiency of a region growing algorithm for segmenting 3-D point clouds.
To apply these methods to industrial object analysis.

Main Methods:

Review of existing surface representation techniques.
Development and analysis of a region growing algorithm.
Utilizing spatial proximity graphs to guide segmentation.
Segmentation of 3-D point sets into planar faces.

Main Results:

Demonstrated efficiency of the region growing algorithm in segmenting 3-D point data.
Successful generation of surface representations from intrinsic images.
Effective application of the method to industrial object datasets.

Conclusions:

The proposed region growing algorithm with spatial proximity graphs is efficient for 3-D object surface representation.
This approach enables accurate segmentation of point clouds into planar faces.
The technique shows practical utility in industrial scene analysis and object recognition.