Dual Branch Image-Guided Network with Multi-stage Iterative Refinement for Depth Completion
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0855-0_7
Reference25 articles.
1. Liu, L., et al.: FCFR-Net: Feature fusion based coarse-to-fine residual learning for monocular depth completion. In: 35th AAAI Conference on Artificial Intelligence, pp. 2136–2144. AAAI, Virtual (2021)
2. Hu, M., Wang, S., Li, B., Ning, S., Fan, L., Gong, X.: PENet: Towards precise and efficient image guided depth completion. In: IEEE International Conference on Robotics and Automation, pp. 13656–13662. IEEE, Xi’an, China (2021)
3. Du, W., Chen, H., Yang, H., Zhang, Y.: Depth completion using geometry-aware embedding. In: 39th International Conference on Robotics and Automation, pp. 8680–8686. IEEE, Philadelphia, PA, USA (2022)
4. Li, Y., Jung, C.: Deep sparse depth completion using joint depth and normal estimation. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE, Monterey, CA, USA, (2023)
5. Jeon, J., Lim, H., Seo, D., Myung, H.: Struct-MDC: Mesh-refined unsupervised depth completion leveraging structural regularities from visual SLAM. IEEE Robot. Autom. Let. 7(3), 6391–6398 (2022)
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