704 citations · 4.1k across the 112 of their papers we have counts for
10 papers · 2 filters
Patch-based Progressive 3D Point Set Upsampling
Wang Yifan, Shihao Wu, Hui Huang +2
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by…
Deep Video-Based Performance Cloning
Kfir Aberman, Mingyi Shi, Jing Liao +3
We present a new video-based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor…
Specular-to-Diffuse Translation for Multi-View Reconstruction
Shihao Wu, Hui Huang, Tiziano Portenier +4
Most multi-view 3D reconstruction algorithms, especially when shape-from-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restrictio…
EC-Net: an Edge-aware Point set Consolidation Network
Lequan Yu, Xianzhi Li, Chi-Wing Fu +2
Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware…
Unsupervised Natural Image Patch Learning
Dov Danon, Hadar Averbuch-Elor, Ohad Fried +1
Learning a metric of natural image patches is an important tool for analyzing images. An efficient means is to train a deep network to map an image patch to a vector space, in whic…
DiDA: Disentangled Synthesis for Domain Adaptation
Jinming Cao, Oren Katzir, Peng Jiang +4
Unsupervised domain adaptation aims at learning a shared model for two related, but not identical, domains by leveraging supervision from a source domain to an unsupervised target…
Neural Best-Buddies: Sparse Cross-Domain Correspondence
Kfir Aberman, Jing Liao, Mingyi Shi +3
Correspondence between images is a fundamental problem in computer vision, with a variety of graphics applications. This paper presents a novel method for sparse cross-domain corre…
Clustering-driven Deep Embedding with Pairwise Constraints
Sharon Fogel, Hadar Averbuch-Elor, Jacov Goldberger +1
Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have b…
Outlier Detection for Robust Multi-dimensional Scaling
Leonid Blouvshtein, Daniel Cohen-Or
Multi-dimensional scaling (MDS) plays a central role in data-exploration, dimensionality reduction and visualization. State-of-the-art MDS algorithms are not robust to outliers, yi…
PU-Net: Point Cloud Upsampling Network
Lequan Yu, Xianzhi Li, Chi-Wing Fu +2
Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud ups…