SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
http://link.springer.com/content/pdf/10.1007/s11263-019-01258-1.pdf
Reference46 articles.
1. Bailer, C., Taetz, B., & Stricker, D. (2015). Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. In International conference on computer vision (ICCV).
2. Bailer, C., Taetz, B., & Stricker, D. (2019). Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. Transactions on Pattern Analysis and Machine Intelligence (PAMI), 41, 1879–1892.
3. Basha, T., Moses, Y., & Kiryati, N. (2013). Multi-view scene flow estimation: A view centered variational approach. International Journal of Computer Vision (IJCV), 101, 6–21.
4. Behl, A., Jafari, O. H., Mustikovela, S. K., Alhaija, H. A., Rother, C., & Geiger, A. (2017). Bounding boxes, segmentations and object coordinates: How important is recognition for 3D scene flow estimation in autonomous driving scenarios? In Conference on computer vision and pattern recognition (CVPR).
5. Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004). High accuracy optical flow estimation based on a theory for warping. In European conference on computer vision (ECCV).
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