A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Shape Reconstruction of a Nonrigid Transport Object Using Refraction and Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Single Camera Stereo using Planar Parallel Plate
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Reflection Stereo - Novel Monocular Stereo using a Transparent Plate -
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
A Refractive Camera for Acquiring Stereo and Super-resolution Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular Range Estimation through a Double-Sided Half-Mirror Plate
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-calibrating depth from refraction
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.00 |
In this paper, we introduce a novel method for depth acquisition based on refraction of light. A scene is captured directly by a camera and by placing a transparent medium between the scene and the camera. A depth map of the scene is then recovered from the displacements of scene points in the images. Unlike other existing depth from refraction methods, our method does not require prior knowledge of the pose and refractive index of the transparent medium, but instead can recover them directly from the input images. By analyzing the displacements of corresponding scene points in the images, we derive closed form solutions for recovering the pose of the transparent medium and develop an iterative method for estimating the refractive index of the medium. Experimental results on both synthetic and real-world data are presented, which demonstrate the effectiveness of the proposed method.