Fetching the paper…
Reading the bibliography…
In this paper, we formulate a potentially valuable panoramic depth completion (PDC) task as panoramic 3D cameras often produce 360{\deg} depth with missing data in complex scenes.
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research 11
2010
Earlier work this paper cites.
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from rgbd images. In: ECCV. pp. 746–760. Springer (2012)
2012
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Li, J., Zhang, T., Luo, W., Yang, J., Yuan, X.T., Zhang, J.: Sparseness analysis in the pretraining of deep neural networks. IEEE transactions on neural networks and learning systems 28
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., Zhang, Y.: Matterport3d: Learning from rgb-d data in indoor environments. In: 3DV (2017)
2017
Earlier work this paper cites.
Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: CVPR. pp. 1746–1754 (2017)
2017
Earlier work this paper cites.
Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.: Sparsity invariant cnns. In: 3DV. pp. 11–20 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: NeurlPS. vol. 30 (2017)
2017
Earlier work this paper cites.
Cheng, X., Wang, P., Yang, R.: Learning depth with convolutional spatial propagation network. In: ECCV. pp. 103–119 (2018)
2018
Earlier work this paper cites.
Chodosh, N., Wang, C., Lucey, S.: Deep convolutional compressed sensing for lidar depth completion. In: ACCV. pp. 499–513. Springer (2018)
2018
Earlier work this paper cites.
Jaritz, M., De Charette, R., Wirbel, E., Perrotton, X., Nashashibi, F.: Sparse and dense data with cnns: Depth completion and semantic segmentation. In: 3DV. pp. 52–60 (2018)
2018
Earlier work this paper cites.
Tateno, K., Navab, N., Tombari, F.: Distortion-aware convolutional filters for dense prediction in panoramic images. In: ECCV. pp. 707–722 (2018)
2018
Earlier work this paper cites.
Zioulis, N., Karakottas, A., Zarpalas, D., Daras, P.: Omnidepth: Dense depth estimation for indoors spherical panoramas. In: ECCV. pp. 448–465 (2018)
2018
Earlier work this paper cites.
Chao, P., Kao, C.Y., Ruan, Y.S., Huang, C.H., Lin, Y.L.: Hardnet: A low memory traffic network. In: ICCV. pp. 3552–3561 (2019)
2019
Earlier work this paper cites.
Eder, M., Moulon, P., Guan, L.: Pano popups: Indoor 3d reconstruction with a plane-aware network. In: 3DV. pp. 76–84. IEEE (2019)
2019
Earlier work this paper cites.
Eldesokey, A., Felsberg, M., Khan, F.S.: Confidence propagation through cnns for guided sparse depth regression. IEEE transactions on pattern analysis and machine intelligence 42
2019
Earlier work this paper cites.
Gordon, A., Li, H., Jonschkowski, R., Angelova, A.: Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras. In: ICCV. pp. 8977–8986 (2019)
2019
Earlier work this paper cites.
Lee, Y., Jeong, J., Yun, J., Cho, W., Yoon, K.J.: Spherephd: Applying cnns on a spherical polyhedron representation of 360deg images. In: CVPR. pp. 9181–9189 (2019)
2019
Earlier work this paper cites.
Ma, F., Cavalheiro, G.V., Karaman, S.: Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera. In: ICRA (2019)
2019
Earlier work this paper cites.
Qiu, J., Cui, Z., Zhang, Y., Zhang, X., Liu, S., Zeng, B., Pollefeys, M.: Deeplidar: Deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image. In: CVPR. pp. 3313–3322 (2019)
2019
Earlier work this paper cites.
Van Gansbeke, W., Neven, D., De Brabandere, B., Van Gool, L.: Sparse and noisy lidar completion with rgb guidance and uncertainty. In: MVA. pp. 1–6 (2019)
2019
Earlier work this paper cites.
Xu, Y., Zhu, X., Shi, J., Zhang, G., Bao, H., Li, H.: Depth completion from sparse lidar data with depth-normal constraints. In: ICCV. pp. 2811–2820 (2019)
2019
Earlier work this paper cites.
Zioulis, N., Karakottas, A., Zarpalas, D., Alvarez, F., Daras, P.: Spherical view synthesis for self-supervised 360 depth estimation. In: 3DV. pp. 690–699. IEEE (2019)
2019
Cited alongside, same era.
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., Sutskever, I.: Generative pretraining from pixels. In: ICML. pp. 1691–1703. PMLR (2020)
2020
Cited alongside, same era.
Cheng, X., Wang, P., Guan, C., Yang, R.: Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion. In: AAAI. pp. 10615–10622 (2020)
2020
Cited alongside, same era.
Feng, B.Y., Yao, W., Liu, Z., Varshney, A.: Deep depth estimation on 360 images with a double quaternion loss. In: 3DV. pp. 524–533. IEEE (2020)
2020
Cited alongside, same era.
Jin, L., Xu, Y., Zheng, J., Zhang, J., Tang, R., Xu, S., Yu, J., Gao, S.: Geometric structure based and regularized depth estimation from 360 indoor imagery. In: CVPR. pp. 889–898 (2020)
Liu, L., Song, X., Lyu, X., Diao, J., Wang, M., Liu, Y., Zhang, L.: Fcfr-net: Feature fusion based coarse-to-fine residual learning for depth completion. In: AAAI. vol. 35, pp. 2136–2144 (2021)
2021
Later among the works it cites.
Pintore, G., Agus, M., Almansa, E., Schneider, J., Gobbetti, E.: Slicenet: deep dense depth estimation from a single indoor panorama using a slice-based representation. In: CVPR. pp. 11536–11545 (2021)
2021
Later among the works it cites.
Rey-Area, M., Yuan, M., Richardt, C.: 360monodepth: High-resolution 360° monocular depth estimation. arXiv e-prints pp. arXiv–2111 (2021)
2021
Later among the works it cites.
Schuster, R., Wasenmuller, O., Unger, C., Stricker, D.: Ssgp: Sparse spatial guided propagation for robust and generic interpolation. In: WACV. pp. 197–206 (2021)
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
Lee, Y., Jeong, J., Yun, J., Cho, W., Yoon, K.J.: Spherephd: Applying cnns on 360° images with non-euclidean spherical polyhedron representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
2020
Cited alongside, same era.
Li, A., Yuan, Z., Ling, Y., Chi, W., Zhang, C., et al.: A multi-scale guided cascade hourglass network for depth completion. In: WACV. pp. 32–40 (2020)
2020
Cited alongside, same era.
Lu, K., Barnes, N., Anwar, S., Zheng, L.: From depth what can you see? depth completion via auxiliary image reconstruction. In: CVPR. pp. 11306–11315 (2020)
2020
Cited alongside, same era.
Park, J., Joo, K., Hu, Z., Liu, C.K., Kweon, I.S.: Non-local spatial propagation network for depth completion. In: ECCV (2020)
2020
Cited alongside, same era.
Tang, J., Tian, F.P., Feng, W., Li, J., Tan, P.: Learning guided convolutional network for depth completion. IEEE Transactions on Image Processing 30
2020
Cited alongside, same era.
Wang, F.E., Yeh, Y.H., Sun, M., Chiu, W.C., Tsai, Y.H.: Bifuse: Monocular 360 depth estimation via bi-projection fusion. In: CVPR. pp. 462–471 (2020)
2020
Cited alongside, same era.
Wong, A., Fei, X., Tsuei, S., Soatto, S.: Unsupervised depth completion from visual inertial odometry. IEEE Robotics and Automation Letters 5
2020
Cited alongside, same era.
Shen, Z., Lin, C., Nie, L., Liao, K., Zhao, Y.: Distortion-tolerant monocular depth estimation on omnidirectional images using dual-cubemap. In: ICME. pp. 1–6. IEEE (2021)
2021
Later among the works it cites.
Sun, C., Hsiao, C.W., Wang, N.H., Sun, M., Chen, H.T.: Indoor panorama planar 3d reconstruction via divide and conquer. In: CVPR. pp. 11338–11347 (2021)
2021
Later among the works it cites.
Sun, C., Sun, M., Chen, H.T.: Hohonet: 360 indoor holistic understanding with latent horizontal features. In: CVPR. pp. 2573–2582 (2021)
2021
Later among the works it cites.
Teutscher, D., Mangat, P., Wasenmüller, O.: Pdc: Piecewise depth completion utilizing superpixels. In: ITSC. pp. 2752–2758. IEEE (2021)
2021
Later among the works it cites.
Wong, A., Cicek, S., Soatto, S.: Learning topology from synthetic data for unsupervised depth completion. IEEE Robotics and Automation Letters 6
2021
Later among the works it cites.
Wong, A., Fei, X., Hong, B.W., Soatto, S.: An adaptive framework for learning unsupervised depth completion. IEEE Robotics and Automation Letters 6
2021
Later among the works it cites.
Wong, A., Soatto, S.: Unsupervised depth completion with calibrated backprojection layers. In: ICCV (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Yan, L., Liu, K., Gao, L.: Dan-conv: Depth aware non-local convolution for lidar depth completion. Electronics Letters 57
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Zhao, S., Gong, M., Fu, H., Tao, D.: Adaptive context-aware multi-modal network for depth completion. IEEE Transactions on Image Processing (2021)
2021
Later among the works it cites.
Zhou, K., Yang, K., Wang, K.: Panoramic depth estimation via supervised and unsupervised learning in indoor scenes. Applied Optics 60
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Closest in time.
Feng, Q., Shum, H.P., Morishima, S.: 360 depth estimation in the wild–the depth360 dataset and the segfuse network. In: VR. IEEE (2022)
2022
Closest in time.
Lin, Y., Cheng, T., Zhong, Q., Zhou, W., Yang, H.: Dynamic spatial propagation network for depth completion. In: AAAI (2022)
2022
Closest in time.
Shen, Z., Lin, C., Liao, K., Nie, L., Zheng, Z., Zhao, Y.: Panoformer: Panorama transformer for indoor 360 depth estimation. arXiv e-prints pp. arXiv–2203 (2022)
2022
Closest in time.
Zhuang, C., Lu, Z., Wang, Y., Xiao, J., Wang, Y.: Acdnet: Adaptively combined dilated convolution for monocular panorama depth estimation. In: AAAI (2022)
2022
Closest in time.