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We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object.
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Mitra, N., Wand, M., Zhang, H., Cohen-Or, D., Bokeloh, M.: Structure-aware shape processing. In: Eurographics State-of-the-art Report (STAR) (2013)
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Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: ShapeNet: An information-rich 3D model repository. arXiv (2015)
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Huang, Q., Wang, H., Koltun, V.: Single-view reconstruction via joint analysis of image and shape collections. ACM TOG 34
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
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Tulsiani, S., Su, H., Guibas, L.J., Efros, A.A., Malik, J.: Learning shape abstractions by assembling volumetric primitives. In: CVPR (2017)
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Kato, H., Ushiku, Y., Harada, T.: Neural 3d mesh renderer. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3907–3916 (2018)
2018
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Niu, C., Li, J., Xu, K.: Im2Struct: Recovering 3D shape structure from a single RGB image. In: CVPR (2018)
2018
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Mo, K., Guerrero, P., Yi, L., Su, H., Wonka, P., Mitra, N., Guibas, L.: StructureNet: Hierarchical graph networks for 3d shape generation. ACM TOG 38
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Paschalidou1, D., Ulusoy, A.O., Geiger, A.: Superquadrics revisited: Learning 3D shape parsing beyond cuboids. In: CVPR (2019)
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Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32
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Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. In: Advances in Neural Information Processing Systems (2019)
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Chaudhuri, S., Ritchie, D., Wu, J., Xu, K., Zhang, H.: Learning generative models of 3d structures. Computer Graphics Forum (Eurographics STAR) (2020)
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Chen, Z., Tagliasacchi, A., Zhang, H.: BSP-Net: Generating compact meshes via binary space partitioning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 45–54 (2020)
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Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G., Tagliasacchi, A.: CvxNet: Learnable convex decomposition. In: CVPR (2020)
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Kania, K., Zieba, M., Kajdanowicz, T.: Ucsg-net-unsupervised discovering of constructive solid geometry tree. Advances in Neural Information Processing Systems 33
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Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3504–3515 (2020)
2020
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Paschalidou, D., Gool, L.V., Geiger, A.: Learning unsupervised hierarchical part decomposition of 3d objects from a single rgb image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1060–1070 (2020)
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Xie, Y., Takikawa, T., Saito, S., Litany, O., Yan, S., Khan, N., Tombari, F., Tompkin, J., Sitzmann, V., Sridhar, S.: Neural fields in visual computing and beyond. Comput. Graph. Forum (2022)
2022
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Yang, Z., Ren, Z., Bautista, M.A., Zhang, Z., Shan, Q., Huang, Q.: Fvor: Robust joint shape and pose optimization for few-view object reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2497–2507 (2022)
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Yu, F., Chen, Z., Li, M., Sanghi, A., Shayani, H., Mahdavi-Amiri, A., Zhang, H.: CAPRI-Net: Learning compact CAD shapes with adaptive primitive assembly. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11768–11778 (2022)
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Daxuan Ren, J.Z., Cai, J., Jiatong Li, H.J., Cai, Z., Zhang, J., Pan, L., Zhang, M., Zhao, H., Yi, S.: CSG-Stump: A learning friendly csg-like representation for interpretable shape parsing. In: ICCV. pp. 12458–12467 (2021)
2021
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Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM 65
2021
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Paschalidou, D., Katharopoulos, A., Geiger, A., Fidler, S.: Neural parts: Learning expressive 3d shape abstractions with invertible neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3204–3215 (2021)
2021
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Wang, Q., Wang, Z., Genova, K., Srinivasan, P., Zhou, H., Barron, J.T., Martin-Brualla, R., Snavely, N., Funkhouser, T.: IBRNet: Learning multi-view image-based rendering. In: CVPR (2021)
2021
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Yu, A., Ye, V., Tancik, M., Kanazawa, A.: PixelNeRF: Neural radiance fields from one or few images. In: CVPR (2021)
2021
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Cai, S., Obukhov, A., Dai, D., Gool, L.V.: Pix2NeRF: Unsupervised conditional π \pi -gan for single image to neural radiance fields translation. In: CVPR (2022)
2022
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Deng, K., Liu, A., Zhu, J.Y., Ramanan, D.: Depth-Supervised NeRF: Fewer views and faster training for free. In: CVPR (2022)
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2022
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2022
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Alaniz, S., Mancini, M., Akata, Z.: Iterative superquadric recomposition of 3d objects from multiple views. In: ICCV (2023)
2023
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2023
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Ganeshan, A., Jones, R.K., Ritchie, D.: Improving unsupervised visual program inference with code rewriting families. In: ICCV (2023)
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Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3D gaussian splatting for real-time radiance field rendering. ACM Trans. on Graphics (SIGGRAPH) (2023)
2023
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Li, P., Guo, J., Zhang, X., ming Yan, D.: SECAD-Net: Self-supervised CAD reconstruction by learning sketch-extrude operations. In: CVPR (2023)
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Liu, R., Wu, R., Hoorick, B.V., Tokmakov, P., Zakharov, S., Vondrick, C.: Zero-1-to-3: Zero-shot one image to 3d object. In: CVPR (2023)
2023
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Monnier, T., Austin, J., Kanazawa, A., Efros, A.A., Aubry, M.: Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives. In: NeurIPS (2023)
2023
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Ren, Y., Zhang, T., Pollefeys, M., Süsstrunk, S., Wang, F.: Volrecon: Volume rendering of signed ray distance functions for generalizable multi-view reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16685–16695 (2023)
2023
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2023
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Tertikas, K., Paschalidou, D., Pan, B., Park, J.J., Uy, M.A., Emiris, I., Avrithis, Y., Guibas, L.: Generating part-aware editable 3d shapes without 3d supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4466–4478 (2023)
2023
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Truong, P., Rakotosaona, M.J., Manhardt, F., Tombari, F.: SPARF: Neural radiance fields from sparse and noisy poses. In: CVPR (2023)
2023
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Vora, A., Patil, A.G., Zhang, H.: DiViNeT: 3D reconstruction from disparate views via neural template regularization. In: NeurIPS (2023)
2023
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Yang, J., Pavone, M., Wang, Y.: FreeNeRF: Improving few-shot neural rendering with free frequency regularization. In: CVPR (2023)
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Yu, F., Chen, Q., Tanveer, M., Mahdavi-Amiri, A., Zhang, H.: D 2 CSG: Unsupervised learning of compact csg trees with dual complements and dropouts. In: NeurIPS (2023)
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