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of 108
pro vyhledávání: '"Chu, Hung‐Kuo"'
Autor:
Pan, Yi-Ting, Lee, Chai-Rong, Fan, Shu-Ho, Su, Jheng-Wei, Huang, Jia-Bin, Chuang, Yung-Yu, Chu, Hung-Kuo
The entertainment industry relies on 3D visual content to create immersive experiences, but traditional methods for creating textured 3D models can be time-consuming and subjective. Generative networks such as StyleGAN have advanced image synthesis,
Externí odkaz:
http://arxiv.org/abs/2402.05728
We present an end-to-end deep learning framework for indoor panoramic image inpainting. Although previous inpainting methods have shown impressive performance on natural perspective images, most fail to handle panoramic images, particularly indoor sc
Externí odkaz:
http://arxiv.org/abs/2301.05624
Recently, differentiable volume rendering in neural radiance fields (NeRF) has gained a lot of popularity, and its variants have attained many impressive results. However, existing methods usually assume the scene is a homogeneous volume so that a ra
Externí odkaz:
http://arxiv.org/abs/2211.14799
Reconstructing 3D layouts from multiple $360^{\circ}$ panoramas has received increasing attention recently as estimating a complete layout of a large-scale and complex room from a single panorama is very difficult. The state-of-the-art method, called
Externí odkaz:
http://arxiv.org/abs/2210.11419
Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly. Although these learning-based methods have shown
Externí odkaz:
http://arxiv.org/abs/2005.10825
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world. It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction m
Externí odkaz:
http://arxiv.org/abs/2005.03372
Akademický článek
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Autor:
Zou, Chuhang, Su, Jheng-Wei, Peng, Chi-Han, Colburn, Alex, Shan, Qi, Wonka, Peter, Chu, Hung-Kuo, Hoiem, Derek
Recent approaches for predicting layouts from 360 panoramas produce excellent results. These approaches build on a common framework consisting of three steps: a pre-processing step based on edge-based alignment, prediction of layout elements, and a p
Externí odkaz:
http://arxiv.org/abs/1910.04099
We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama. To achieve better prediction accuracy, our method leverages two projections of the panorama at once, namely the equirectangu
Externí odkaz:
http://arxiv.org/abs/1811.11977
Autor:
Wang, Fu-En, Hu, Hou-Ning, Cheng, Hsien-Tzu, Lin, Juan-Ting, Yang, Shang-Ta, Shih, Meng-Li, Chu, Hung-Kuo, Sun, Min
As 360{\deg} cameras become prevalent in many autonomous systems (e.g., self-driving cars and drones), efficient 360{\deg} perception becomes more and more important. We propose a novel self-supervised learning approach for predicting the omnidirecti
Externí odkaz:
http://arxiv.org/abs/1811.05304