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Cited by 179

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Real-time range acquisition by adaptive structured light

Koninckx, T.P., Van Gool, L.·2006·IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

The goal of this paper is to provide a "self-adaptive" system for real-time range acquisition. Reconstructions are based on a single frame structured light illumination. Instead of using generic, static coding that is supposed to work under all circumstances, system adaptation is proposed. This occurs on-the-fly and renders the system more robust against instant scene variability and creates suitable patterns at startup. A continuous trade-off between speed and quality is made. A weighted combination of different coding cues--based upon pattern color, geometry, and tracking--yields a robust way to solve the correspondence problem. The individual coding cues are automatically adapted within a considered family of patterns. The weights to combine them are based on the average consistency with the result within a small time-window. The integration itself is done by reformulating the problem as a graph cut. Also, the camera-projector configuration is taken into account for generating the projection patterns. The correctness of the range maps is not guaranteed, but an estimation of the uncertainty is provided for each part of the reconstruction. Our prototype is implemented using unmodified consumer hardware only and, therefore, is cheap. Frame rates vary between 10 and 25 fps, dependent on scene complexity.

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Metadata

Authors
Koninckx, T. P., & Van Gool, L.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2006-03
Volume
28
Issue
3
Pages
432-445
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2141844174
Cited by
179
References
1
FWCI
8.63
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Real-time range acquisition by adaptive structured light. https://app.cochat.ai/papers/10.1109/tpami.2006.62
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Koninckx, T. P., & Van Gool, L. (2006). Real-time range acquisition by adaptive structured light. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2006.62

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