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Reconstructing and editing 3D objects and scenes both play crucial roles in computer graphics and computer vision.
J. T. Kajiya, “The rendering equation,” in Proceedings of the 13th annual conference on Computer graphics and interactive techniques , 1986, pp. 143–150
1986
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Transactions on Image Processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
B. Walter, S. R. Marschner, H. Li, and K. E. Torrance, “Microfacet models for refraction through rough surfaces,” in Eurographics conference on Rendering Techniques , 2007, pp. 195–206
2007
Earlier work this paper cites.
O. Sorkine-Hornung and M. Alexa, “As-rigid-as-possible surface modeling,” in Symposium on Geometry Processing , 2007
2007
Earlier work this paper cites.
B. Burley and W. D. A. Studios, “Physically-based shading at Disney,” in ACM SIGGRAPH , 2012, pp. 1–7
2012
Earlier work this paper cites.
B. Karis, “Real shading in Unreal engine 4,” 2013
2013
Earlier work this paper cites.
B. O. Community, Blender - a 3D modelling and rendering package , Blender Foundation, 2018. [Online]. Available: http://www.blender.org
2018
Earlier work this paper cites.
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, “Occupancy networks: Learning 3D reconstruction in function space,” in CVPR , 2019, pp. 4460–4470
2019
Earlier work this paper cites.
Z. Chen and H. Zhang, “Learning implicit fields for generative shape modeling,” in CVPR , 2019, pp. 5939–5948
2019
Earlier work this paper cites.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “DeepSDF: Learning continuous signed distance functions for shape representation,” in CVPR , 2019, pp. 165–174
2019
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 CVPR , 2018, pp. 586–595
2019
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,” in ECCV , 2020, pp. 405–421
2020
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 CVPR , 2020, pp. 3501–3512
2020
Earlier work this paper cites.
L. Liu, J. Gu, K. Zaw Lin, T.-S. Chua, and C. Theobalt, “Neural sparse voxel fields,” Advances in Neural Information Processing Systems , pp. 15 651–15 663, 2020
2020
Earlier work this paper cites.
F. Xiang, Z. Xu, M. Hasan, Y. Hold-Geoffroy, K. Sunkavalli, and H. Su, “NeuTex: Neural texture mapping for volumetric neural rendering,” in CVPR , 2021, pp. 7119–7128
2021
Earlier work this paper cites.
S. Liu, X. Zhang, Z. Zhang, R. Zhang, J.-Y. Zhu, and B. Russell, “Editing conditional radiance fields,” in ICCV , 2021, pp. 5773–5783
2021
Earlier work this paper cites.
M. Oechsle, S. Peng, and A. Geiger, “UNISURF: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction,” in ICCV , 2021, pp. 5589–5599
2021
Earlier work this paper cites.
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,” in Advances in Neural Information Processing Systems , 2021, pp. 27 171–27 183
2021
Earlier work this paper cites.
L. Yariv, J. Gu, Y. Kasten, and Y. Lipman, “Volume rendering of neural implicit surfaces,” in Advances in Neural Information Processing Systems , 2021, pp. 4805–4815
2021
Earlier work this paper cites.
P. P. Srinivasan, B. Deng, X. Zhang, M. Tancik, B. Mildenhall, and J. T. Barron, “NeRV: Neural reflectance and visibility fields for relighting and view synthesis,” in CVPR , 2021, pp. 7495–7504
2021
Earlier work this paper cites.
M. Boss, R. Braun, V. Jampani, J. T. Barron, C. Liu, and H. Lensch, “NeRD: Neural reflectance decomposition from image collections,” in ICCV , 2021, pp. 12 684–12 694
2021
Earlier work this paper cites.
K. Zhang, F. Luan, Q. Wang, K. Bala, and N. Snavely, “PhySG: Inverse rendering with spherical gaussians for physics-based material editing and relighting,” in CVPR , 2021, pp. 5453–5462
2021
Cited alongside, same era.
X. Zhang, P. P. Srinivasan, B. Deng, P. Debevec, W. T. Freeman, and J. T. Barron, “NeRFactor: Neural factorization of shape and reflectance under an unknown illumination,” ACM Trans. Graph. , vol. 40, no. 6, pp. 1–18, 2021
2021
Cited alongside, same era.
T. Shen, J. Gao, K. Yin, M.-Y. Liu, and S. Fidler, “Deep marching tetrahedra: a hybrid representation for high-resolution 3D shape synthesis,” in Advances in Neural Information Processing Systems , 2021, pp. 6087–6101
2021
Cited alongside, same era.
C. Bao, B. Yang, Z. Junyi, B. Hujun, Z. Yinda, C. Zhaopeng, and Z. Guofeng, “NeuMesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing,” in ECCV , 2022, pp. 597–614
2022
Cited alongside, same era.
2023
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2023
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2023
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2023
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR , 2022, pp. 10 674–10 685
2022
Cited alongside, same era.
C. Sun, M. Sun, and H. Chen, “Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction,” in CVPR , 2022, pp. 5449–5459
2022
Cited alongside, same era.
A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “TensoRF: Tensorial radiance fields,” in ECCV , vol. 13692, 2022, pp. 333–350
2022
Cited alongside, same era.
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Trans. Graph. , vol. 41, no. 4, pp. 102:1–102:15, 2022
2022
Cited alongside, same era.
D. Verbin, P. Hedman, B. Mildenhall, T. E. Zickler, J. T. Barron, and P. P. Srinivasan, “Ref-NeRF: Structured view-dependent appearance for neural radiance fields,” in CVPR , 2022, pp. 5481–5490
2022
Cited alongside, same era.
Y. Zhang, J. Sun, X. He, H. Fu, R. Jia, and X. Zhou, “Modeling indirect illumination for inverse rendering,” in CVPR , 2022, pp. 18 622–18 631
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. Munkberg, W. Chen, J. Hasselgren, A. Evans, T. Shen, T. Müller, J. Gao, and S. Fidler, “Extracting triangular 3D models, materials, and lighting from images,” in CVPR , 2022, pp. 8270–8280
2022
Cited alongside, same era.
Z. Chen, T. Funkhouser, P. Hedman, and A. Tagliasacchi, “MobileNeRF: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures,” in CVPR , 2023
2023
Later among the works it cites.
L. Yariv, P. Hedman, C. Reiser, D. Verbin, P. P. Srinivasan, R. Szeliski, J. T. Barron, and B. Mildenhall, “BakedSDF: Meshing neural sdfs for real-time view synthesis,” in ACM SIGGRAPH 2023 Conference Proceedings , 2023, pp. 46:1–46:9
2023
Later among the works it cites.
Y. Liu, L. Wang, J. Yang, W. Chen, X. Meng, B. Yang, and L. Gao, “NeUDF: Leaning neural unsigned distance fields with volume rendering,” in CVPR , 2023, pp. 237–247
2023
Later among the works it cites.
X. Long, C. Lin, L. Liu, Y. Liu, P. Wang, C. Theobalt, T. Komura, and W. Wang, “NeuralUDF: Learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies,” in CVPR , 2023, pp. 20 834–20 843
2023
Later among the works it cites.
X. Meng, W. Chen, and B. Yang, “NeAT: Learning neural implicit surfaces with arbitrary topologies from multi-view images,” in CVPR , 2023, pp. 248–258
2023
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2023
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2023
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2023
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2023
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A. Mai, D. Verbin, F. Kuester, and S. Fridovich-Keil, “Neural microfacet fields for inverse rendering,” 2023
2023
Later among the works it cites.
H. Jin, I. Liu, P. Xu, X. Zhang, S. Han, S. Bi, X. Zhou, Z. Xu, and H. Su, “TensoIR: Tensorial inverse rendering,” in CVPR , 2023
2023
Later among the works it cites.
Y. Liu, P. Wang, C. Lin, X. Long, J. Wang, L. Liu, T. Komura, and W. Wang, “NeRO: Neural geometry and BRDF reconstruction of reflective objects from multiview images,” ACM Trans. Graph. , vol. 42, no. 4, pp. 114:1–114:22, 2023
2023
Later among the works it cites.
Z. Wang, T. Shen, J. Gao, S. Huang, J. Munkberg, J. Hasselgren, Z. Gojcic, W. Chen, and S. Fidler, “Neural fields meet explicit geometric representations for inverse rendering of urban scenes,” in CVPR , 2023, pp. 8370–8380
2023
Later among the works it cites.
J. Sun, T. Wu, Y. Yang, Y. Lai, and L. Gao, “SOL-NeRF: Sunlight modeling for outdoor scene decomposition and relighting,” in SIGGRAPH Asia 2023 Conference Papers . ACM, 2023, pp. 31:1–31:11
2023
Later among the works it cites.
Z. Kuang, Y. Zhang, H. Yu, S. Agarwala, S. Wu, and J. Wu, “Stanford-ORB: A real-world 3d object inverse rendering benchmark,” in NeurIPS 2023 , 2023
2023
Later among the works it cites.
K. Ye, Q. Hou, and K. Zhou, “3d gaussian splatting with deferred reflection,” in ACM SIGGRAPH 2024 Conference Proceedings . ACM, 2024
2024
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