The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Quantitative Evaluation of Confidence Measures for Stereo Vision
Abstract
We present an extensive evaluation of 17 confidence measures for stereo matching that compares the most widely used measures as well as several novel techniques proposed here. We begin by categorizing these methods according to which aspects of stereo cost estimation they take into account and then assess their strengths and weaknesses. The evaluation is conducted using a winner-take-all framework on binocular and multibaseline datasets with ground truth. It measures the capability of each confidence method to rank depth estimates according to their likelihood for being correct, to detect occluded pixels, and to generate low-error depth maps by selecting among multiple hypotheses for each pixel. Our work was motivated by the observation that such an evaluation is missing from the rapidly maturing stereo literature and that our findings would be helpful to researchers in binocular and multiview stereo.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.19 · Cited by 2,457
High-accuracy stereo depth maps using structured light
Scharstein, D., & Szeliski, R. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211354 · Cited by 1,542
Cited by
High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth
Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., & Westling, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-11752-2_3 · Cited by 1,305
High-quality streamable free-viewpoint video
Collet, A., Chuang, M., Sweeney, P., Gillett, D., Evseev, D., Calabrese, D., Hoppe, H., Kirk, A., & Sullivan, S. (2015) · ACM Transactions on Graphics · doi:10.1145/2766945 · Cited by 573
Intel(R) RealSense(TM) Stereoscopic Depth Cameras
Keselman, L., Woodfill, J. I., Grunnet-Jepsen, A., & Bhowmik, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.167 · Cited by 448
A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation
Laga, H., Jospin, L. V., Boussaid, F., & Bennamoun, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3032602 · Cited by 336
Learning Confidence Measures by Multi-modal Convolutional Neural Networks
Fu, Z., & Ardabilian Fard, M. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00149 · Cited by 43
Metadata
- Authors
- Hu, X., & Mordohai, P.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2012-11
- Volume
- 34
- Issue
- 11
- Pages
- 2121-2133
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1973871580
- Cited by
- 261
- References
- 3
- FWCI
- 18.03
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
