Paper Info
Title
VeIGAN: Vectorial Inpainting Generative Adversarial Network for Depth Maps Object Removal
Abstract
The recent precision increase in image-based depth estimation encourages to use this type of data for mapping. Recent work proposes different approaches to deal with the problem of occlusion generated by different scene perspectives of stereo cameras. However, there is less attention to depth estimation and inpainting for object removal and object occlusion. In this paper, we study recent inpainting approaches for RGB images and apply these methods on depth maps. We propose a Generative Adversarial Network (GAN) for depth feature extraction to estimate the depth inside a masked area, in order to remove objects on disparity images. Our results show that using depth features on the loss function and on the network architecture, increase the result precision and give to the generated image a depth distribution close to the real data. Our main contribution is a GAN, which estimates depth information in a masked area inside a disparity image.
Year
DOI
Venue
2019
10.1109/IVS.2019.8814157
2019 IEEE Intelligent Vehicles Symposium (IV)
Keywords
Field
DocType
depth features,network architecture,depth distribution,depth information,masked area,disparity image,image-based depth estimation,stereo cameras,object occlusion,RGB images,depth feature extraction,inpainting approaches,scene perspectives,vectorial inpainting generative adversarial network,depth map object removal
Computer vision,Stereo cameras,Generative adversarial network,Computer science,Network architecture,Inpainting,Feature extraction,Artificial intelligence,RGB color model
Conference
ISSN
ISBN
Citations 
1931-0587
978-1-7281-0561-1
0
PageRank 
References 
Authors
0.34
10
5
Name
Order
Citations
PageRank
Lucas P. N. Matias100.34
Marc Sons201.35
Jefferson R. Souza3467.19
Denis F. Wolf431130.16
Christoph Stiller52189153.23