[56] V. Sitzmann, J. Thies, F. Heide, M. Nießner, G. Wetzstein, and M. Zollhofer. Deepvoxels: Learning persistent 3d feature embeddings. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2437-2446, 2019.
[57] J. Sun, Z. Shen, Y. Wang, H. Bao, and X. Zhou. Loftr: Detector-free local feature matching with transformers. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8922-8931, 2021.
[58] X. Sun, J. Wu, X. Zhang, Z. Zhang, C. Zhang, T. Xue, J. B. Tenenbaum, and W. T. Freeman. Pix3D: Dataset and methods for single-image 3D shape modeling. In CVPR, 2018.
[59] M. Tatarchenko, S. R. Richter, R. Ranftl, Z. Li, V. Koltun, and T. Brox. What do single-view 3d reconstruction networks learn? In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3405-3414, 2019.
[60] P. Wang, L. Liu, Y. Liu, C. Theobalt, T. Komura, and W. Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. Advances in Neural Information Processing Systems (NeurIPS), 2021.
[61] P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona. Caltech-ucsd birds 200, 2010.
[62] D. A. Williams. three.js. URL https://threejs.org/.
[63] J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In Advances in Neural Information Processing Systems (NeurIPS), 2016.
[64] T. Wu, J. Zhang, X. Fu, Y. Wang, J. Ren, L. Pan, W. Wu, L. Yang, J. Wang, C. Qian, et al. Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation. arXiv preprint arXiv:2301.07525, 2023.
[65] Y. Xiang, R. Mottaghi, and S. Savarese. Beyond Pascal: A benchmark for 3d object detection in the wild. In IEEE winter conference on applications of computer vision, pages 75-82. IEEE, 2014.
[66] H. Xie, H. Yao, X. Sun, S. Zhou, and S. Zhang. Pix2vox: Context-aware 3d reconstruction from single and multi-view images. In IEEE International Conference on Computer Vision (ICCV), 2019.
[67] H. Xie, H. Yao, S. Zhang, S. Zhou, and W. Sun. Pix2vox++: Multi-scale context-aware 3d object reconstruction from single and multiple images. International Journal on Computer Vision (IJCV), 2020.
[68] C.-H. Yao, W.-C. Hung, Y. Li, M. Rubinstein, M.-H. Yang, and V. Jampani. Lassie: Learning articulated shape from sparse image ensemble via 3d part discovery. In NeurIPS, 2022.
[69] C.-H. Yao, A. Raj, W.-C. Hung, Y. Li, M. Rubinstein, M.-H. Yang, and V. Jampani. Artic3d: Learning robust articulated 3d shapes from noisy web image collections. arXiv:2306.04619, 2023.
[70] Y. Yao, Z. Luo, S. Li, J. Zhang, Y. Ren, L. Zhou, T. Fang, and L. Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[71] A. Yu, V. Ye, M. Tancik, and A. Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[72] X. Yu, M. Xu, Y. Zhang, H. Liu, C. Ye, Y. Wu, Z. Yan, T. Liang, G. Chen, S. Cui, and X. Han. Mvmgren: A large-scale dataset of multi-view images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[73] J. Y. Zhang, G. Yang, S. Tulsiani, and D. Ramanan. NeRS: Neural reflectance surfaces for sparse-view 3d reconstruction in the wild. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
[74] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018.
[75] Q.-Y. Zhou, J. Park, and V. Koltun. Fast global registration. In European Conference on Computer Vision (ECCV), 2016.