PanoFormer: Panorama Transformer for Indoor 360$$^{\circ }$$ Depth Estimation

Author:

Shen Zhijie,Lin Chunyu,Liao Kang,Nie Lang,Zheng Zishuo,Zhao Yao

Publisher

Springer Nature Switzerland

Reference45 articles.

1. Armeni, I., Sax, S., Zamir, A.R., Savarese, S.: Joint 2d–3d-semantic data for indoor scene understanding. arXiv preprint arXiv:1702.01105 (2017)

2. Bhat, S.F., Alhashim, I., Wonka, P.: Adabins: depth estimation using adaptive bins. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4009–4018 (2021)

3. Bhoi, A.: Monocular depth estimation: A survey. arXiv preprint arXiv:1901.09402 (2019)

4. Chang, A., et al.: Matterport3d: Learning from rgb-d data in indoor environments. In: 2017 International Conference on 3D Vision (3DV), pp. 667–676. IEEE Computer Society (2017)

5. Chen, H.X., Li, K., Fu, Z., Liu, M., Chen, Z., Guo, Y.: Distortion-aware monocular depth estimation for omnidirectional images. IEEE Signal Process. Lett. 28, 334–338 (2021)

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