Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Rigid body motion from range image sequences
CVGIP: Image Understanding
Performance of optical flow techniques
International Journal of Computer Vision
Direct Estimation of Range Flow on Deformable Shape From a Video Rate Range Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Differential Range Flow Estimation
Mustererkennung 1999, 21. DAGM-Symposium
Dense Range Flow from Depth and Intensity Data
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
A 3D Optical Flow Approach to Addition of Deformable PET Volumes
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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We present quantitative results for computing local least squares and global regularized range flow using both image and range data. We first review the computation of local least squares range flow and then show how its computation can be cast in a global Horn and Schunck like regularization framework [15]. These computations are done using both range data only and using a combination of image and range data [14]. We present quantitative results for these two least squares range flow algorithms and for the two regularization range flow algorithms for one synthetic range sequence and one real range sequence, where the correct 3D motions are known a priori. We show that using both image and range data produces more accurate and more dense range flow than the use of range flow alone.