Accurate depth image generation via overfit training of point cloud registration using local frame sets

Aldoma, 2012, A global hypotheses verification method for 3d object recognition, 511

Barron, J.T., Malik, J., 2013. Intrinsic scene properties from a single rgb-d image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 17–24.

Bulat, A., Yang, J., Tzimiropoulos, G., 2018. To learn image super-resolution, use a gan to learn how to do image degradation first. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 185–200.

Choy, C., Dong, W., Koltun, V., 2020. Deep global registration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2514–2523.

Choy, C., Park, J., Koltun, V., 2019. Fully convolutional geometric features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8958–8966.

Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M., 2017a. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5828–5839.

Dai, 2017, Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration, ACM Trans. Graph., 36, 1, 10.1145/3072959.3054739

Deng, H., Birdal, T., Ilic, S., 2019. 3d local features for direct pairwise registration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3244–3253.

DeTone, D., Malisiewicz, T., Rabinovich, A., 2018. Superpoint: Self-supervised interest point detection and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 224–236.

Eitel, 2015, Multimodal deep learning for robust RGB-D object recognition, 681

El Banani, M., Gao, L., Johnson, J., 2021. Unsupervisedr&r: Unsupervised point cloud registration via differentiable rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7129–7139.

Ferstl, D., Reinbacher, C., Ranftl, R., Rüther, M., Bischof, H., 2013. Image guided depth upsampling using anisotropic total generalized variation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 993–1000.

Firman, M., 2016. RGBD Datasets: Past, Present and Future. In: CVPR Workshop on Large Scale 3D Data: Acquisition, Modelling and Analysis.

Gojcic, Z., Zhou, C., Wegner, J.D., Guibas, L.J., Birdal, T., 2020. Learning multiview 3d point cloud registration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1759–1769.

Gojcic, Z., Zhou, C., Wegner, J.D., Wieser, A., 2019. The perfect match: 3d point cloud matching with smoothed densities. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5545–5554.

Gu, 2020, Coupled real-synthetic domain adaptation for real-world deep depth enhancement, IEEE Trans. Image Process., 29, 6343

Henry, 2012, RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments, Int. J. Robot. Res., 31, 647, 10.1177/0278364911434148

Huang, X., Mei, G., Zhang, J., 2020. Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11366–11374.

Hui, 2016, Depth map super-resolution by deep multi-scale guidance, 353

Jeon, J., Lee, S., 2018. Reconstruction-based pairwise depth dataset for depth image enhancement using CNN. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 422–438.

Jiang, H., Shen, Y., Xie, J., Li, J., Qian, J., Yang, J., 2021. Sampling network guided cross-entropy method for unsupervised point cloud registration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6128–6137.

Johns, 2016, Deep learning a grasp function for grasping under gripper pose uncertainty, 4461

Johnson, 1999, Using spin images for efficient object recognition in cluttered 3D scenes, IEEE Trans. Pattern Anal. Mach. Intell., 21, 433, 10.1109/34.765655

Kim, 2021, Deformable kernel networks for joint image filtering, Int. J. Comput. Vis., 129, 579, 10.1007/s11263-020-01386-z

Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H., 2017. Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 624–632.

Li, J., Lee, G.H., 2019. Usip: Unsupervised stable interest point detection from 3d point clouds. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 361–370.

Li, L., Zhu, S., Fu, H., Tan, P., Tai, C.-L., 2020. End-to-end learning local multi-view descriptors for 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1919–1928.

Lu, S., Ren, X., Liu, F., 2014. Depth enhancement via low-rank matrix completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3390–3397.

McCormac, J., Handa, A., Leutenegger, S., Davison, A.J., 2017. Scenenet rgb-d: Can 5 m synthetic images beat generic imagenet pre-training on indoor segmentation?. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2678–2687.

Meister, 2012, When can we use kinectfusion for ground truth acquisition, 2, 3

Rusu, 2008, Aligning point cloud views using persistent feature histograms, 3384

Sarlin, P.-E., DeTone, D., Malisiewicz, T., Rabinovich, A., 2020. Superglue: Learning feature matching with graph neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4938–4947.

Schwarz, 2015, RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features, 1329

Shen, X., Zhou, C., Xu, L., Jia, J., 2015. Mutual-structure for joint filtering. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 3406–3414.

Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R., 2017. Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2107–2116.

Song, 2016, Deep depth super-resolution: Learning depth super-resolution using deep convolutional neural network, 360

Sterzentsenko, V., Saroglou, L., Chatzitofis, A., Thermos, S., Zioulis, N., Doumanoglou, A., Zarpalas, D., Daras, P., 2019. Self-supervised deep depth denoising. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1242–1251.

Sweeney, 2019, A supervised approach to predicting noise in depth images, 796

Tian, 2019, ObjectFusion: An object detection and segmentation framework with RGB-D SLAM and convolutional neural networks, Neurocomputing, 345, 3, 10.1016/j.neucom.2019.01.088

Wang, Y., Solomon, J.M., 2019. Deep closest point: Learning representations for point cloud registration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3523–3532.

Wiles, O., Gkioxari, G., Szeliski, R., Johnson, J., 2020. Synsin: End-to-end view synthesis from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7467–7477.

Yan, S., Wu, C., Wang, L., Xu, F., An, L., Guo, K., Liu, Y., 2018. Ddrnet: Depth map denoising and refinement for consumer depth cameras using cascaded cnns. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 151–167.

Yew, Z.J., Lee, G.H., 2018. 3dfeat-net: Weakly supervised local 3d features for point cloud registration. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 607–623.

Yew, Z.J., Lee, G.H., 2020. Rpm-net: Robust point matching using learned features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11824–11833.

Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., Funkhouser, T., 2017. 3dmatch: Learning local geometric descriptors from rgb-d reconstructions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1802–1811.

Zhang, Y., Funkhouser, T., 2018. Deep depth completion of a single rgb-d image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 175–185.

Zhao, Z., Zhang, J., Xu, S., Lin, Z., Pfister, H., 2022. Discrete cosine transform network for guided depth map super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5697–5707.