The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Massively Parallel Multiview Stereopsis by Surface Normal Diffusion
Abstract
We present a new, massively parallel method for high-quality multiview matching. Our work builds on the Patchmatch idea: starting from randomly generated 3D planes in scene space, the best-fitting planes are iteratively propagated and refined to obtain a 3D depth and normal field per view, such that a robust photo-consistency measure over all images is maximized. Our main novelties are on the one hand to formulate Patchmatch in scene space, which makes it possible to aggregate image similarity across multiple views and obtain more accurate depth maps. And on the other hand a modified, diffusion-like propagation scheme that can be massively parallelized and delivers dense multiview correspondence over ten 1.9-Megapixel images in 3 seconds, on a consumer-grade GPU. Our method uses a slanted support window and thus has no fronto-parallel bias, it is completely local and parallel, such that computation time scales linearly with image size, and inversely proportional to the number of parallel threads. Furthermore, it has low memory footprint (four values per pixel, independent of the depth range). It therefore scales exceptionally well and can handle multiple large images at high depth resolution. Experiments on the DTU and Middlebury multiview datasets as well as oblique aerial images show that our method achieves very competitive results with high accuracy and completeness, across a range of different scenarios.
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Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
Stereo Processing by Semiglobal Matching and Mutual Information
Hirschmuller, H. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,195
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
PatchMatch: a randomized correspondence algorithm for structural image editing
Barnes, C., Shechtman, E., Finkelstein, A., & Goldman, D. B. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531330 · Cited by 2,880
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
Screened poisson surface reconstruction
Kazhdan, M., & Hoppe, H. (2013) · ACM Transactions on Graphics · doi:10.1145/2487228.2487237 · Cited by 2,231
Poisson Surface Reconstruction
Kazhdan, M. M., Bolitho, M., & Hoppe, H. (2006) · doi:10.2312/sgp/sgp06/061-070 · Cited by 2,125
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
Tola, E., Lepetit, V., & Fua, P. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.77 · Cited by 1,405
Cited by
Pixelwise View Selection for Unstructured Multi-View Stereo
Schönberger, J. L., Zheng, E., Frahm, J.-M., & Pollefeys, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_31 · Cited by 2,315
MVSNet: Depth Inference for Unstructured Multi-view Stereo
Yao, Y., Luo, Z., Li, S., Fang, T., & Quan, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_47 · Cited by 1,455
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
Self-citationRecurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference
Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., & Quan, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00567 · Cited by 666
Neuralangelo: High-Fidelity Neural Surface Reconstruction
Li, Z., Müller, T., Evans, A., Taylor, R. H., Unberath, M., Liu, M.-Y., & Lin, C.-H. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00817 · Cited by 441
Metadata
- Authors
- Galliani, S., Lasinger, K., & Schindler, K.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 873-881
- OpenAlex
- W2205172244
- Cited by
- 599
- References
- 41
- FWCI
- 6.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Galliani, S., Lasinger, K., & Schindler, K. (2015). Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.106
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