Abstract
3D modelling finds a wide range of applications in industry. However, due to the presence of surface scanning noise, accumulative registration
errors, and improper data fusion, reconstructed object surfaces using range images captured from multiple viewpoints are often distorted with
thick patches, false connections, blurred features and artefacts. Moreover, the existing integration methods are often expensive in the sense
of both computational time and data storage. These shortcomings limit the wide applications of 3D modelling using the latest laser scanning
systems. In this paper, the k-means clustering approach (from the pattern recognition and machine learning literatures) is employed to minimize
the integration error and to optimize the fused point locations. To initialize the clustering approach, an automatic method is developed, shifting
points in the overlapping areas between neighbouring views towards each other, so that the initialized cluster centroids are in between the
two overlapping surfaces. This results in more efficient and effective integration of data. While the overlapping areas were initially detected
using a single distance threshold, they are then refined using the k-means clustering method. For more accurate integration results, a weighting
scheme reflecting the imaging principle is developed to integrate the corresponding points in the overlapping areas. The fused point set is
finally triangulated using an improved Delaunay method, guaranteeing a watertight surface. A comparative study based on real images shows
that the proposed algorithm is efficient in the sense of either running time or memory usage and reduces significantly the integration error,
while desirably retaining geometric details of 3D object surfaces of interest.
Original language | English |
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Pages (from-to) | 152-175 |
Number of pages | 24 |
Journal | Pattern Recognition |
Volume | 41 |
Issue number | 1 |
Early online date | 07 Jul 2007 |
DOIs | |
Publication status | Published - 01 Jan 2008 |
Keywords
- 3D modelling
- Registered range images
- k-Means clustering
- Point shift
- Overlapping area detection and integration
- Surface details