Unsupervised Scale-Consistent Depth Learning from Video

Author:

Bian Jia-WangORCID,Zhan Huangying,Wang Naiyan,Li Zhichao,Zhang Le,Shen Chunhua,Cheng Ming-Ming,Reid Ian

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Reference95 articles.

1. Baker, S., & Matthews, I. (2004). Lucas-kanade 20 years on: A unifying framework. International Journal on Computer Vision (IJCV), 56(3), 221–225.

2. Bian, J., Lin, W.-Y., Liu, Y., Zhang, L., Yeung, S.-K., Cheng, M.-M., et al. (2020a). GMS: Grid-based motion statistics for fast, ultra-robust feature correspondence. International Journal on Computer Vision (IJCV), 4(2), 4181–4190.

3. Bian, J.-W., Li, Z., Wang, N., Zhan, H., Shen, C., Cheng, M.-M., et al. (2019a). Unsupervised scale-consistent depth and ego-motion learning from monocular video. In Neural Information Processing Systems (NeurIPS).

4. Bian, J.-W., Wu, Y.-H., Zhao, J., Liu, Y., Zhang, L., Cheng, M.-M., et al. (2019b). An evaluation of feature matchers for fundamental matrix estimation. In British Machine Vision Conference (BMVC).

5. Bian, J.-W., Zhan, H., Wang, N., Chin, T.-J., Shen, C., & Reid, I. (2020b). Unsupervised depth learning in challenging indoor video: Weak rectification to rescue. arXiv preprintarXiv:2006.02708.

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