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• [Lin2022] Kai-En Lin, Lin Yen-Chen, Wei-Sheng Lai, Tsung-Yi Lin, Yi-Chang Shih, and Ravi Ramamoorthi. Vision transformer for nerf-based view synthesis from a single input image. arXiv
preprint arXiv:2207.05736, 2022.
• [Ling2022] Selena Ling, Nicholas Sharp, and Alec Jacobson. Vectoradam for rotation equiv- ariant geometry optimization. arXiv preprint arXiv:2205.13599, 2022.
• [Liu2019a] Liu, Shichen, et al. "Soft rasterizer: A differentiable renderer for image-based 3d reasoning." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.
• [Liu2019b] Liu, Shichen, et al. "Learning to infer implicit surfaces without 3d supervision." NeurIPS 2019.
• [Liu2022] Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, Sanja Fidler, and Or Litany. Learning smooth neural functions via lipschitz regularization. SIGGRAPH, 2022.
• [Loper2014] Loper, Matthew M., and Michael J. Black. "OpenDR: An approximate differentiable renderer." European Conference on Computer Vision. Springer, Cham, 2014.
• [Ma2021] Ma, Qianli, et al. "SCALE: Modeling clothed humans with a surface codec of articulated local elements." Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition. 2021.
• [Maturana2015] Maturana, Daniel, and Sebastian Scherer. "Voxnet: A 3d convolutional neural network for real-time object recognition." 2015 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS). IEEE, 2015.
• [Mescheder2019] Mescheder, Lars, et al. "Occupancy networks: Learning 3d reconstruction in function space." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition. 2019.
• [Mildenhall2020] Mildenhall, Ben, et al. "Nerf: Representing scenes as neural radiance fields for view synthesis." European conference on computer vision. Springer, Cham, 2020.
• [Miangoleh2021] Miangoleh, S. Mahdi H., et al. "Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging." Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
• [Mueller2022] Thomas Mueller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint
arXiv:2201.05989, 2022.
• [Newell2016] Newell, Alejandro, Kaiyu Yang, and Jia Deng. "Stacked hourglass networks for human pose estimation." European conference on computer vision. Springer, Cham, 2016.
• [Nicolet2021] Baptiste Nicolet, Alec Jacobson, and Wenzel Jakob. Large steps in inverse rendering of geometry. ACM Transactions on Graphics (TOG), Vol. 40, No. 6, pp. 1–13, 2021.
• [Park2019] Park, Jeong Joon, et al. "Deepsdf: Learning continuous signed distance functions for shape representation." Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition. 2019.
• [Peng2020] Peng, Songyou, et al. "Convolutional occupancy networks." Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part
III 16. Springer International Publishing, 2020.
• [Qi2016] Qi, Charles R., et al. "Volumetric and multi-view cnns for object classification on 3d data." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
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