АЛГОРИТМИ ШТУЧНОГО ІНТЕЛЕКТУ У 3D МОДЕЛЮВАННІ
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Goodfellow I., Pouget-Abadie J., Mirza M. et al. Generative Adversarial Nets // Advances in Neural Information Processing Systems. 2014. URL: https://arxiv.org/abs/1406.2661
Tancik M., Srinivasan P., Mildenhall B. et al. Neural Radiance Fields for View Synthesis // Communications of the ACM. 2022. URL: https://arxiv.org/abs/2003.08934
Chan E. R., Monteiro M., Kellnhofer P. et al. GET3D: A Generative Model of High Quality 3D Textured Shapes // Proceedings of CVPR. 2022. URL: https://arxiv.org/abs/2209.11163
Poole B., Jain A., Barron J. T., Mildenhall B. DreamFusion: Text-to-3D using 2D Diffusion // arXiv preprint. 2022. URL: https://arxiv.org/abs/2209.14988
Mescheder L., Oechsle M., Niemeyer M. et al. Occupancy Networks: Learning 3D Reconstruction in Function Space // Proceedings of CVPR. 2019. URL: https://arxiv.org/abs/1812.03828
Park J. J., Florence P., Straub J. et al. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation // Proceedings of CVPR. 2019. URL: https://arxiv.org/abs/1901.05103
Nichol A., Dhariwal P. Improved Denoising Diffusion Probabilistic Models // International Conference on Machine Learning (ICML). 2021. URL: https://arxiv.org/abs/2102.09672
