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In Proceedings of the ieee\/cvf international conference on computer vision (pp. 12747\u201312756).","DOI":"10.1109\/ICCV48922.2021.01251"},{"issue":"4","key":"2089_CR171","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3323024","volume":"38","author":"B Wronski","year":"2019","unstructured":"Wronski, B., Garcia-Dorado, I., Ernst, M., Kelly, D., Krainin, M., Liang, C.-K., & Milanfar, P. (2019). Handheld multi-frame super-resolution. ACM Transactions on Graphics (ToG), 38(4), 1\u201318.","journal-title":"ACM Transactions on Graphics (ToG)"},{"key":"2089_CR172","doi-asserted-by":"crossref","unstructured":"Wu, X. , Peng, L. , Yang, H. , Xie, L. , Huang, C. , Deng, C. , & Cai, D. (2022). Sparse fuse dense: Towards high quality 3d detection with depth completion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Proceedings of the ieee\/cvf conference on computer vision and pattern recognition (pp. 5418\u20135427).","DOI":"10.1109\/CVPR52688.2022.00534"},{"key":"2089_CR173","doi-asserted-by":"crossref","unstructured":"Xia, F. , Zamir, A.R. , He, Z. , Sax, A. , Malik, J. , & Savarese, S. (2018). Gibson env: Real-world perception for embodied agents. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 9068\u20139079).","DOI":"10.1109\/CVPR.2018.00945"},{"key":"2089_CR174","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3201534","author":"Z Xie","year":"2022","unstructured":"Xie, Z., Yu, X., Gao, X., Li, K., & Shen, S. (2022). Recent advances in conventional and deep learning-based depth completion: A survey. 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