Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks

24 June 2021

Papers citing "Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks"

8 / 8 papers shown

Title
Dr.Bokeh: DiffeRentiable Occlusion-aware Bokeh Rendering Yichen Sheng Zixun Yu Lu Ling Zhiwen Cao Cecilia Zhang Xin Lu Ke Xian Haiting Lin Bedrich Benes 28 9 0 17 Aug 2023
GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild Chao Wang Ana Serrano Xingang Pan Bin Chen Hans-Peter Seidel Christian Theobalt K. Myszkowski Thomas Leimkuehler GAN 23 15 0 22 Nov 2022
AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields Takuhiro Kaneko 20 14 0 13 Jun 2022
Shape and Viewpoint without Keypoints Shubham Goel Angjoo Kanazawa Jitendra Malik 3DV 89 108 0 21 Jul 2020
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 262 10,344 0 12 Dec 2018
Synthetic Depth-of-Field with a Single-Camera Mobile Phone Neal Wadhwa Rahul Garg David E. Jacobs Bryan E. Feldman Nori Kanazawa Robert E. Carroll Yair Movshovitz-Attias Jonathan T. Barron Yael Pritch M. Levoy 3DH MDE 177 176 0 11 Jun 2018
Deep Ordinal Regression Network for Monocular Depth Estimation Huan Fu Mingming Gong Chaohui Wang Kayhan Batmanghelich Dacheng Tao MDE 180 1,705 0 06 Jun 2018
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Jiajun Wu Chengkai Zhang Tianfan Xue Bill Freeman J. Tenenbaum GAN 164 1,940 0 24 Oct 2016