A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Trinocular Active Range-Sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward optimal structured light patterns
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
A multibaseline stereo system with active illumination and real-time image acquisition
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Color Stereo Vision Using Hierarchical Block Matching and Active Color Illumination
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Capturing 2½D Depth and Texture of Time-Varying Scenes Using Structured Infrared Light
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Real-Time Range Acquisition by Adaptive Structured Light
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Accurate Real-Time Disparity Estimation with Variational Methods
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Joint estimation of motion, structure and geometry from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
Space-time image sequence analysis: object tunnels and occlusion volumes
IEEE Transactions on Image Processing
Binocular structured light stereo matching approach for dense facial disparity map
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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This paper addresses the problem of space-time stereo with active illumination and presents a formulation of this problem in the variational framework. Variational problems of this scale are computationally expensive to solve directly. We overcome this challenge by showing that speed-improving techniques, as the full-multi-grid and the multi-leveladaptation techniques, can be applied. We evaluate the performance of our method on 3 ground-truth datasets. The experimental results for synthetic and real datasets show that the combination of active illumination and variational space-time stereo improves the quality of the reconstruction on average by up to 3.1 times compared to a reconstruction from a single passive stereo image pair without active illumination.