activity
20152023
most citedJSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds

134 citations · 513 across the 41 of their papers we have counts for

collaborators
Showing 2020Show all

8 papers · 1 filter

cs.CV2020

Scale-aware Insertion of Virtual Objects in Monocular Videos

Songhai Zhang, Xiangli Li, Yingtian Liu +1

In this paper, we propose a scale-aware method for inserting virtual objects with proper sizes into monocular videos. To tackle the scale ambiguity problem of geometry recovery fro…

cs.CV2020★ 134 cited

JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds

Zeyu Hu, Mingmin Zhen, Xuyang Bai +2

Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision. Despite the fast evolution of learning-based 3D sema…

cs.GR2020★ 4 cited

Deep Generation of Face Images from Sketches

Shu-Yu Chen, Wanchao Su, Lin Gao +2

Recent deep image-to-image translation techniques allow fast generation of face images from freehand sketches. However, existing solutions tend to overfit to sketches, thus requiri…

cs.CV2020★ 5 cited

End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds

Lei Li, Siyu Zhu, Hongbo Fu +2

In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-…

cs.CV2020★ 27 cited

D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features

Xuyang Bai, Zixin Luo, Lei Zhou +3

A successful point cloud registration often lies on robust establishment of sparse matches through discriminative 3D local features. Despite the fast evolution of learning-based 3D…

cs.GR2020★ 21 cited

Human Motion Transfer with 3D Constraints and Detail Enhancement

Yang-Tian Sun, Qian-Cheng Fu, Yue-Ren Jiang +4

We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of…

cs.CV2020★ 5 cited

SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

Lumin Yang, Jiajie Zhuang, Hongbo Fu +3

We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph, w…

cs.CV2020★ 3 cited

SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondence

Deng Yu, Lei Li, Youyi Zheng +4

In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as ou…