The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Large Scale Multi-view Stereopsis Evaluation
Abstract
The seminal multiple view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis methodology. Although seminal, these benchmark datasets are limited in scope with few reference scenes. Here, we try to take these works a step further by proposing a new multi-view stereo dataset, which is an order of magnitude larger in number of scenes and with a significant increase in diversity. Specifically, we propose a dataset containing 80 scenes of large variability. Each scene consists of 49 or 64 accurate camera positions and reference structured light scans, all acquired by a 6-axis industrial robot. To apply this dataset we propose an extension of the evaluation protocol from the Middlebury evaluation, reflecting the more complex geometry of some of our scenes. The proposed dataset is used to evaluate the state of the art multiview stereo algorithms of Tola et al., Campbell et al. and Furukawa et al. Hereby we demonstrate the usability of the dataset as well as gain insight into the workings and challenges of multi-view stereopsis. Through these experiments we empirically validate some of the central hypotheses of multi-view stereopsis, as well as determining and reaffirming some of the central challenges.
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Citations
Cites
Accurate, Dense, and Robust Multiview Stereopsis
Furukawa, Y., & Ponce, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.161 · Cited by 3,019
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
Poisson Surface Reconstruction
Kazhdan, M. M., Bolitho, M., & Hoppe, H. (2006) · doi:10.2312/sgp/sgp06/061-070 · Cited by 2,125
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · cited 1×
“On the other hand, point-cloud based methods convert photo-consistency data into a 3D pointcloud, which is then converted into a 3D surface using standard meshing techniques such as Poisson reconstruction [9], Graph-cuts [20] or signed distance functions [13].”
MethodHigh-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
Pattern codification strategies in structured light systems
Salvi, J., Pagès, J., & Batlle, J. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2003.10.002 · Cited by 1,110
A state of the art in structured light patterns for surface profilometry
Salvi, J., Fernandez, S., Pribanic, T., & Llado, X. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.03.004 · Cited by 855
On benchmarking camera calibration and multi-view stereo for high resolution imagery
Strecha, C., von Hansen, W., Van Gool, L., Fua, P., & Thoennessen, U. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587706 · Cited by 814
Towards Internet-scale multi-view stereo
Furukawa, Y., Curless, B., Seitz, S. M., & Szeliski, R. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539802 · Cited by 751
Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo
Campbell, N. D. F., Vogiatzis, G., Hernández, C., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_58 · Cited by 390
Self-citationEfficient large-scale multi-view stereo for ultra high-resolution image sets
Tola, E., Strecha, C., & Fua, P. (2012) · Machine Vision and Applications · doi:10.1007/s00138-011-0346-8 · Cited by 328
Self-citationGoesele, M., Curless, B., & Seitz, S. M. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.199 · Cited by 308
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency
Vogiatzis, G., Hernandez Esteban, C., Torr, P. H. S., & Cipolla, R. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70712 · Cited by 284
Self-citationTowards high-resolution large-scale multi-view stereo
Hiep, V. H., Keriven, R., Labatut, P., & Pons, J.-P. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206617 · Cited by 228
Towards high-resolution large-scale multi-view stereo
Hiep, V. H., Keriven, R., Labatut, P., & Pons, J.-P. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206617 · Cited by 106
Dense reconstruction on-the-fly
Wendel, A., Maurer, M., Graber, G., Pock, T., & Bischof, H. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247833 · Cited by 105
Probabilistic visibility for multi-view stereo
Hernandez, C., Vogiatzis, G., & Cipolla, R. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383193 · Cited by 98
Self-citationFast Joint Estimation of Silhouettes and Dense 3D Geometry from Multiple Images
Kolev, K., Brox, T., & Cremers, D. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.150 · Cited by 78
Scale Robust Multi View Stereo
Bailer, C., Finckh, M., & Lensch, H. P. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33712-3_29 · Cited by 63
Evaluation of stereo confidence indoors and outdoors
Hu, X., & Mordohai, P. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539798 · Cited by 48
Cited by
Multi-view learning overview: Recent progress and new challenges
Zhao, J., Xie, X., Xu, X., & Sun, S. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.007 · Cited by 1,002
A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos
Schops, T., Schonberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., & Geiger, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.272 · Cited by 915
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
Oechsle, M., Peng, S., & Geiger, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00554 · Cited by 622
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
Niemeyer, M., Barron, J. T., Mildenhall, B., Sajjadi, M. S. M., Geiger, A., & Radwan, N. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00540 · Cited by 609
Massively Parallel Multiview Stereopsis by Surface Normal Diffusion
Galliani, S., Lasinger, K., & Schindler, K. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.106 · Cited by 599
Metadata
- Authors
- Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., & Aanaes, H.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 406-413
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2085905957
- Cited by
- 729
- References
- 20
- FWCI
- 8.83
- Type
- conference paper
- Language
- en
- Open access
- closed
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