Synthetic images generation for semantic understanding in facility management

Understanding Synthetic Aperture Radar Images

Semantic Foggy Scene Understanding with Synthetic Data

This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding with weatherclear images, little attention has been paid to SFSU. Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict weather-clear outdoor sc...

SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes

We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic segmentation is built on a deep autoencoder stack trained exclusively on synthetic depth data generated from our novel 3D scene library, SynthCam3D. Importantly, our network is able to segment real world scenes without a...

Automatic Generation of Synthetic Retinal Fundus Images

This study aims to generate synthetic and realistic retinal fundus colour images, similar in characteristics to a given dataset, as well as the values of all morphological parameters. A representative task could be, for example, the synthesis of a retinal image with the corresponding vessel tree and optic nerve head binary map, measurement of vessel width in any position, fovea localisation and...

Semantic Building Information Model and Multimedia for Facility Management

In the field of civil engineering, the proliferation of stakeholders and the heterogeneity of modeling tools detract from the quality of the design process, construction and building maintenance. In this paper, we present a Web-based platform lets geographically dispersed project participants—from facility managers and architects to electricians to plumbers—directly use and exchange project doc...