ControLRM: Fast and Controllable 3D Generation via Large Reconstruction Model

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J. Canny, “A computational approach to edge detection,” IEEE Transactions on pattern analysis and machine intelligence , no. 6, pp. 679–698, 1986
1986
Earlier work this paper cites.
M. Goesele, N. Snavely, B. Curless, H. Hoppe, and S. M. Seitz, “Multi-view stereo for community photo collections,” in 2007 IEEE 11th International Conference on Computer Vision . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4104–4113
2016
Earlier work this paper cites.
J. Lei Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” ArXiv e-prints , pp. arXiv–1607, 2016
2016
Earlier work this paper cites.
A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
M. Arjovsky and L. Bottou, “Towards principled methods for training generative adversarial networks,” arXiv preprint arXiv:1701.04862 , 2017
Original
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan, “Mvsnet: Depth inference for unstructured multi-view stereo,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 767–783
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
2018
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4401–4410
2019
Earlier work this paper cites.
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger, “Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 3504–3515
2020
Earlier work this paper cites.
L. Yariv, Y. Kasten, D. Moran, M. Galun, M. Atzmon, B. Ronen, and Y. Lipman, “Multiview neural surface reconstruction by disentangling geometry and appearance,” Advances in Neural Information Processing Systems , vol. 33, pp. 2492–2502, 2020
2020
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020
Original
2020
Earlier work this paper cites.
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 3, pp. 1623–1637, 2020
2020
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International conference on machine learning . Pmlr, 2021, pp. 8821–8831
2021
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Earlier work this paper cites.
H. Xu, Z. Zhou, Y. Qiao, W. Kang, and Q. Wu, “Self-supervised multi-view stereo via effective co-segmentation and data-augmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 4, 2021, pp. 3030–3038
2021
Earlier work this paper cites.
H. Xu, Z. Zhou, Y. Wang, W. Kang, B. Sun, H. Li, and Y. Qiao, “Digging into uncertainty in self-supervised multi-view stereo,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6078–6087
2021
Earlier work this paper cites.
J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 5855–5864
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685 , 2021
Original
2021
Earlier work this paper cites.
G. Bae, I. Budvytis, and R. Cipolla, “Estimating and exploiting the aleatoric uncertainty in surface normal estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 13 137–13 146
2021
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Earlier work this paper cites.
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,” arXiv preprint arXiv:2209.14988 , 2022
Original
2022
Earlier work this paper cites.
A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “Tensorf: Tensorial radiance fields,” in European conference on computer vision . Springer, 2022, pp. 333–350
2022
Earlier work this paper cites.

Similar

E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis et al. , “Efficient geometry-aware 3d generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 16 123–16 133
2022
Cited alongside, same era.
H. Wang, X. Du, J. Li, R. A. Yeh, and G. Shakhnarovich, “Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation,” 2022. [Online]. Available: https://arxiv.org/abs/2212.00774
Original
2022
Cited alongside, same era.
A. Nichol, H. Jun, P. Dhariwal, P. Mishkin, and M. Chen, “Point-e: A system for generating 3d point clouds from complex prompts,” 2022. [Online]. Available: https://arxiv.org/abs/2212.08751
Original
2022
Cited alongside, same era.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” 2022
2022
Cited alongside, same era.

Then

J. Li, H. Tan, K. Zhang, Z. Xu, F. Luan, Y. Xu, Y. Hong, K. Sunkavalli, G. Shakhnarovich, and S. Bi, “Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model,” 2023. [Online]. Available: https://arxiv.org/abs/2311.06214
Original
2023
Later among the works it cites.
H. Jun and A. Nichol, “Shap-e: Generating conditional 3d implicit functions,” 2023. [Online]. Available: https://arxiv.org/abs/2305.02463
Original
2023
Later among the works it cites.
Z. Tang, S. Gu, C. Wang, T. Zhang, J. Bao, D. Chen, and B. Guo, “Volumediffusion: Flexible text-to-3d generation with efficient volumetric encoder,” arXiv preprint arXiv:2312.11459 , 2023
Original
2023
Later among the works it cites.
J. Lorraine, K. Xie, X. Zeng, C.-H. Lin, T. Takikawa, N. Sharp, T.-Y. Lin, M.-Y. Liu, S. Fidler, and J. Lucas, “Att3d: Amortized text-to-3d object synthesis,” 2023. [Online]. Available: https://arxiv.org/abs/2306.07349
Original
2023
Later among the works it cites.

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ControLRM: Fast and Controllable 3D Generation via Large Reconstruction Model · Around

Built on

J. Canny, “A computational approach to edge detection,” IEEE Transactions on pattern analysis and machine intelligence , no. 6, pp. 679–698, 1986
1986
Earlier work this paper cites.
M. Goesele, N. Snavely, B. Curless, H. Hoppe, and S. M. Seitz, “Multi-view stereo for community photo collections,” in 2007 IEEE 11th International Conference on Computer Vision . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4104–4113
2016
Earlier work this paper cites.
J. Lei Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” ArXiv e-prints , pp. arXiv–1607, 2016
2016
Earlier work this paper cites.
A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
M. Arjovsky and L. Bottou, “Towards principled methods for training generative adversarial networks,” arXiv preprint arXiv:1701.04862 , 2017
Original
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan, “Mvsnet: Depth inference for unstructured multi-view stereo,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 767–783
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
2018
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4401–4410
2019
Earlier work this paper cites.
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger, “Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 3504–3515
2020
Earlier work this paper cites.
L. Yariv, Y. Kasten, D. Moran, M. Galun, M. Atzmon, B. Ronen, and Y. Lipman, “Multiview neural surface reconstruction by disentangling geometry and appearance,” Advances in Neural Information Processing Systems , vol. 33, pp. 2492–2502, 2020
2020
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020
Original
2020
Earlier work this paper cites.
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 3, pp. 1623–1637, 2020
2020
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International conference on machine learning . Pmlr, 2021, pp. 8821–8831
2021
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Earlier work this paper cites.
H. Xu, Z. Zhou, Y. Qiao, W. Kang, and Q. Wu, “Self-supervised multi-view stereo via effective co-segmentation and data-augmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 4, 2021, pp. 3030–3038
2021
Earlier work this paper cites.
H. Xu, Z. Zhou, Y. Wang, W. Kang, B. Sun, H. Li, and Y. Qiao, “Digging into uncertainty in self-supervised multi-view stereo,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6078–6087
2021
Earlier work this paper cites.
J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 5855–5864
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685 , 2021
Original
2021
Earlier work this paper cites.
G. Bae, I. Budvytis, and R. Cipolla, “Estimating and exploiting the aleatoric uncertainty in surface normal estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 13 137–13 146
2021
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Earlier work this paper cites.
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,” arXiv preprint arXiv:2209.14988 , 2022
Original
2022
Earlier work this paper cites.
A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “Tensorf: Tensorial radiance fields,” in European conference on computer vision . Springer, 2022, pp. 333–350
2022
Earlier work this paper cites.

Similar

E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis et al. , “Efficient geometry-aware 3d generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 16 123–16 133
2022
Cited alongside, same era.
H. Wang, X. Du, J. Li, R. A. Yeh, and G. Shakhnarovich, “Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation,” 2022. [Online]. Available: https://arxiv.org/abs/2212.00774
Original
2022
Cited alongside, same era.
A. Nichol, H. Jun, P. Dhariwal, P. Mishkin, and M. Chen, “Point-e: A system for generating 3d point clouds from complex prompts,” 2022. [Online]. Available: https://arxiv.org/abs/2212.08751
Original
2022
Cited alongside, same era.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” 2022
2022
Cited alongside, same era.

Then

J. Li, H. Tan, K. Zhang, Z. Xu, F. Luan, Y. Xu, Y. Hong, K. Sunkavalli, G. Shakhnarovich, and S. Bi, “Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model,” 2023. [Online]. Available: https://arxiv.org/abs/2311.06214
Original
2023
Later among the works it cites.
H. Jun and A. Nichol, “Shap-e: Generating conditional 3d implicit functions,” 2023. [Online]. Available: https://arxiv.org/abs/2305.02463
Original
2023
Later among the works it cites.
Z. Tang, S. Gu, C. Wang, T. Zhang, J. Bao, D. Chen, and B. Guo, “Volumediffusion: Flexible text-to-3d generation with efficient volumetric encoder,” arXiv preprint arXiv:2312.11459 , 2023
Original
2023
Later among the works it cites.
J. Lorraine, K. Xie, X. Zeng, C.-H. Lin, T. Takikawa, N. Sharp, T.-Y. Lin, M.-Y. Liu, S. Fidler, and J. Lucas, “Att3d: Amortized text-to-3d object synthesis,” 2023. [Online]. Available: https://arxiv.org/abs/2306.07349
Original
2023
Later among the works it cites.

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ControLRM: Fast and Controllable 3D Generation via Large Reconstruction Model

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