Maximum-likelihood registration of range images with missing data
Missing data are common in range images, due to geometric occlusions, limitations in the sensor field of view, poor reflectivity, depth discontinuities, and cast shadows. Using registration to align these data often fails, because points without valid correspondences can be incorrectly matched. This...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1998. - 30(2008), 1 vom: 13. Jan., Seite 120-30 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article |
| Langue: | English |
| Publié: |
2008
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. |
| Résumé: | Missing data are common in range images, due to geometric occlusions, limitations in the sensor field of view, poor reflectivity, depth discontinuities, and cast shadows. Using registration to align these data often fails, because points without valid correspondences can be incorrectly matched. This paper presents a maximum likelihood method for registration of scenes with unmatched or missing data. Using ray casting, correspondences are formed between valid and missing points in each view. These correspondences are used to classify points by their visibility properties, including occlusions, field of view, and shadow regions. The likelihood of each point match is then determined using statistical properties of the sensor, such as noise and outlier distributions. Experiments demonstrate a high rates of convergence on complex scenes with varying degrees of overlap |
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| Description: | Date Completed 12.02.2008 Date Revised 16.11.2007 published: Print Citation Status MEDLINE |
| ISSN: | 0162-8828 |