Abstract
Our research focuses on creating a framework for extracting 3D deformable objects from 2D scenes. We research the possibility of using multiple graph convolutional operators and depth estimators to extract the object, while also using predefined segmentation masks for the objects in the images. The experiments focus on a dataset from 2017, containing all the requirements of having ground truths for segmentation, depth estimation and the resulting object. The best results we obtained were using a smaller version of MiDaS and the DeGraRec_SAGE variant, on random selections of images from the dataset.
Citare
@Inproceedings{Alexandrescu2024ContRailAF,
author = {Andrei-Robert Alexandrescu and Răzvan-Gabriel Petec and Alexandru Manole and Laura Diosan},
booktitle = {arXiv.org},
title = {ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet},
year = {2024}
}