and tracking," in International Symposium on Mixed and Augmented Reality, pp. 127-136, IEEE, 2011.
[28] M. Nießner, M. Zollhöfer, S. Izadi, and M. Stamminger, "Real-time 3d reconstruction at scale using voxel hashing," ACM Transcation on Graphics, vol. 32, no. 6, pp. 169:1-169:11, 2013.
[29] O. Kähler, V. A. Prisacariu, J. P. C. Valentin, and D. W. Murray, "Hierarchical voxel block hashing for efficient integration of depth images," IEEE Robotics Automation Letters, vol. 1, no. 1, pp. 192-197, 2016.
[30] B. Curless and M. Levoy, "A volumetric method for building complex models from range images," in Annual Conference on Computer Graphics and Interactive Techniques, pp. 303-312, ACM, 1996.
[31] M. Ji, J. Gall, H. Zheng, Y. Liu, and L. Fang, "Surfacenet: An end-to-end 3d neural network for multiview stereopsis," in International Conference on Computer Vision, pp. 2326-2334, IEEE, 2017.
[32] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling," in in Neural Information Processing Systems Workshop on Deep Learning, 2014.
[33] L. M. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, "Occupancy networks: Learning 3d reconstruction in function space," in Conference on Computer Vision and Pattern Recognition, pp. 4460-4470, IEEE, 2019.
[34] J. J. Park, P. R. Florence, J. Straub, R. A. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in Conference on Computer Vision and Pattern Recognition, pp. 165–174, IEEE, 2019.
[35] L. Liu, J. Gu, K. Z. Lin, T. Chua, and C. Theobalt, "Neural sparse voxel fields," in Advances in Neural Information Processing Systems, 2020.
[36] X. Zhang, S. Bi, K. Sunkavalli, H. Su, and Z. Xu, “Nerfusion: Fusing radiance fields for large-scale scene reconstruction,” in Conference on Computer Vision and Pattern Recognition, pp. 5439–5448, IEEE, 2022.
[37] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems, pp. 5998–6008, 2017.
[38] H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, "Searching efficient 3d architectures with sparse point-voxel convolution," in European Conference on Computer Vision, pp. 685–702, Springer, 2020.
[39] X. Long, L. Liu, W. Li, C. Theobalt, and W. Wang, "Multi-view depth estimation using epipolar spatio-temporal networks," in Conference on Computer Vision and Pattern Recognition, pp. 8258-8267, IEEE, 2021.
[40] A. Dai, A. X. Chang, M. Savva, M. Halber, T. A. Funkhouser, and M. Nießner, "Scannet: Richly-annotated 3d reconstructions of indoor scenes," in Conference on Computer Vision and Pattern Recognition, pp. 2432–2443, IEEE, 2017.
[41] J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of RGB-D SLAM systems,” in International Conference on Intelligent Robots and Systems, pp. 573–580, IEEE, 2012.
[42] A. Handa, T. Whelan, J. McDonald, and A. J. Davison, "A benchmark for RGB-D visual odometry, 3d reconstruction and SLAM," in International Conference on Robotics and Automation, pp. 1524-1531, IEEE, 2014.
[43] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," in International Conference on Learning Representations (Y. Bengio and Y. LeCun, eds.), 2015.
[44] M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le, “Mnasnet: Platform-aware neural architecture search for mobile,” in Conference on Computer Vision and Pattern Recognition, pp. 2815–2823, 2019.
[45] T. Lin, P. Dollar, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” in Conference on Computer Vision and Pattern Recognition, pp. 936–944, IEEE, 2017.