The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Estimating 3D Shape and Texture Using Pixel Intensity, Edges, Specular Highlights, Texture Constraints and a Prior
Abstract
We present a novel algorithm aiming to estimate the 3D shape, the texture of a human face, along with the 3D pose and the light direction from a single photograph by recovering the parameters of a 3D morphable model. Generally, the algorithms tackling the problem of 3D shape estimation from image data use only the pixels intensity as input to drive the estimation process. This was previously achieved using either a simple model, such as the Lambertian reflectance model, leading to a linear fitting algorithm. Alternatively, this problem was addressed using a more precise model and minimizing a non-convex cost function with many local minima. One way to reduce the local minima problem is to use a stochastic optimization algorithm. However, the convergence properties (such as the radius of convergence) of such algorithms, are limited. Here, as well as the pixel intensity, we use various image features such as the edges or the location of the specular highlights. The 3D shape, texture and imaging parameters are then estimated by maximizing the posterior of the parameters given these image features. The overall cost function obtained is smoother and, hence, a stochastic optimization algorithm is not needed to avoid the local minima problem. This leads to the multi-features fitting algorithm that has a wider radius of convergence and a higher level of precision. This is shown on some example photographs, and on a recognition experiment performed on the CMU-PIE image database.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
A method for registration of 3-D shapes
Besl, P. J., & McKay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 18,103
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 11,811
A morphable model for the synthesis of 3D faces
Blanz, V., & Vetter, T. (1999) · Proceedings of the 26th annual conference on Computer graphics and interactive techniques - SIGGRAPH '99 · doi:10.1145/311535.311556 · Cited by 4,710
Self-citationFace recognition based on fitting a 3D morphable model
Blanz, V., & Vetter, T. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1227983 · Cited by 2,002
Self-citationHierarchical chamfer matching: a parametric edge matching algorithm
Borgefors, G. (1988) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.9107 · Cited by 1,056
Robust registration of 2D and 3D point sets
Fitzgibbon, A. W. (2003) · Image and Vision Computing · doi:10.1016/j.imavis.2003.09.004 · Cited by 925
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
Belhumeur, P. N., & Kriegman, D. J. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008005721484 · Cited by 525
Real-time combined 2D+3D active appearance models
Xiao, J., Baker, S., Matthews, I., & Kanade, T. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315210 · Cited by 416
Linear object classes and image synthesis from a single example image
Vetter, T., & Poggio, T. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598230 · Cited by 409
Self-citationPattern Classification: A Unified View of Statistical and Neural Approaches
Simonson, K. H. (1997) · Technometrics · doi:10.1080/00401706.1997.10485447 · Cited by 384
On Photometric Issues in 3D Visual Recognition from a Single 2D Image
Shashua, A. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007975506780 · Cited by 221
Efficient, robust and accurate fitting of a 3D morphable model
Romdhani, & Vetter (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238314 · Cited by 189
Distance Transforms of Sampled Functions
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · eCommons (Cornell University) · Cited by 151
Cited by
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network
Tran, A. T., Hassner, T., Masi, I., & Medioni, G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.163 · Cited by 503
3D Morphable Face Models—Past, Present, and Future
Egger, B., Smith, W. A. P., Tewari, A., Wuhrer, S., Zollhoefer, M., Beeler, T., Bernard, F., Bolkart, T., Kortylewski, A., Romdhani, S., et al. (2020) · ACM Transactions on Graphics · doi:10.1145/3395208 · Cited by 448
Self-citationFace Alignment in Full Pose Range: A 3D Total Solution
Zhu, X., Liu, X., Lei, Z., & Li, S. Z. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2778152 · Cited by 391
Metadata
- Authors
- Romdhani, S., & Vetter, T.
- Venue
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Publisher
- IEEE
- Published
- 2005-07-27
- Volume
- 2
- Pages
- 986-993
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2155211928
- Cited by
- 299
- References
- 15
- FWCI
- 5.21
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
