Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spacetime Stereo: A Unifying Framework for Depth from Triangulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimised De Bruijn patterns for one-shot shape acquisition
Image and Vision Computing
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Stereo camera systems are widely used in many real applications including indoor and outdoor robotics. They are very easy to use and provide accurate depth estimates on well-textured scenes, but often fail when the scene does not have enough texture. It is possible to help the system work better in this situation by actively projecting certain light patterns to the scene to create artificial texture on the scene surface. The question we try to answer in ths paper is what would be the best pattern(s) to project. This paper introduces optimized projection patterns based on a novel concept of (symmetric) non-recurring De Bruijn sequences, and describes algorithms to generate such sequences. A projected pattern creates an artificial texture which does not contain any duplicate patterns over epipolar lines within certain range, thus it makes the correspondence match simple and unique. The proposed patterns are compatible with most existing stereo algorithms, meaning that they can be used without any changes in the stereo algorithm and one can immediately get much denser depth estimates without any additional computational cost. It is also argued that the proposed patterns are optimal binary patterns, and finally a few experimental result using stereo and space-time stereo algorithms are presented.