IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation of Albedo for Illumination-Invariant Matching and Shape Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of surface normal estimation methods for range sensing applications
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Robust 3D-mapping with time-of-flight cameras
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multipath Interference Compensation in Time-of-Flight Camera Images
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Automatic Real-Time Video Matting Using Time-of-Flight Camera and Multichannel Poisson Equations
International Journal of Computer Vision
SURFing the point clouds: Selective 3D spatial pyramids for category-level object recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Multipath interference of light is the cause of important errors in Time of Flight (ToF) depth estimation. This paper proposes an algorithm that removes multipath distortion from a single depth map obtained by a ToF camera. Our approach does not require information about the scene, apart from ToF measurements. The method is based on fitting ToF measurements with a radiometric model. Model inputs are depth values free from multipath interference whereas model outputs consist of synthesized ToF measurements. We propose an iterative optimization algorithm that obtains model parameters that best reproduce ToF measurements, recovering the depth of the scene without distortion. We show results with both synthetic and real scenes captured by commercial ToF sensors. In all cases, our algorithm accurately corrects the multipath distortion, obtaining depth maps that are very close to ground truth data.