Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
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[1] Jaakko Lehtinen,et al. Differentiable Monte Carlo ray tracing through edge sampling , 2018, ACM Trans. Graph..
[2] Pietro Perona,et al. Caltech-UCSD Birds 200 , 2010 .
[3] Shunyu Yao,et al. 3D-Aware Scene Manipulation via Inverse Graphics , 2018, NeurIPS.
[4] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[5] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[6] William T. Freeman,et al. Unsupervised Training for 3D Morphable Model Regression , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] David Meger,et al. Improved Adversarial Systems for 3D Object Generation and Reconstruction , 2017, CoRL.
[8] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[9] Wei Liu,et al. Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images , 2018, ECCV.
[10] Tatsuya Harada,et al. Neural 3D Mesh Renderer , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[11] Frank D. Luna,et al. Introduction to 3D Game Programming with DirectX 11 , 2008 .
[12] Tom Davis,et al. Opengl programming guide: the official guide to learning opengl , 1993 .
[13] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Attila Szabó,et al. Unsupervised 3D Shape Learning from Image Collections in the Wild , 2018, ArXiv.
[15] Jiajun Wu,et al. Learning Shape Priors for Single-View 3D Completion and Reconstruction , 2018, ECCV.
[16] Alec Jacobson,et al. Paparazzi , 2018, ACM Trans. Graph..
[17] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[18] David Meger,et al. GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects , 2019, ICML.
[19] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[20] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[21] Leonidas J. Guibas,et al. Learning Representations and Generative Models for 3D Point Clouds , 2017, ICML.
[22] Silvio Savarese,et al. Beyond PASCAL: A benchmark for 3D object detection in the wild , 2014, IEEE Winter Conference on Applications of Computer Vision.
[23] Jitendra Malik,et al. Learning Category-Specific Mesh Reconstruction from Image Collections , 2018, ECCV.
[24] Bo Yang,et al. 3D Object Reconstruction from a Single Depth View with Adversarial Learning , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
[25] Daniel Cohen-Or,et al. Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer , 2019, ArXiv.
[26] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[27] Alexey Dosovitskiy,et al. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds , 2018, NeurIPS.
[28] Tianqi Chen,et al. Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.
[29] Gavin S. P. Miller,et al. Hierarchical Z-buffer visibility , 1993, SIGGRAPH.
[30] Chun-Liang Li,et al. Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer , 2018, ICLR.
[31] Thomas Brox,et al. What Do Single-View 3D Reconstruction Networks Learn? , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Hao Li,et al. Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[33] Andrea Vedaldi,et al. Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.
[34] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Vittorio Ferrari,et al. Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision , 2018, BMVC.
[36] Pat Hanrahan,et al. An efficient representation for irradiance environment maps , 2001, SIGGRAPH.
[37] Michael J. Black,et al. OpenDR: An Approximate Differentiable Renderer , 2014, ECCV.
[38] Hao Li,et al. Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction , 2019, ArXiv.
[39] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[40] Jan Kautz,et al. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[41] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[42] Bui Tuong Phong. Illumination for computer generated pictures , 1975, Commun. ACM.
[43] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[44] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.