graph convolutional networks

ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet (2024)

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…

DeGraRec: 3D Deformable Object Reconstruction Using Graph Neural Networks and Depth Estimation (2024)

Obtaining 3D representation of objects from scenes composed out of multiple images or frames is a task that often reacquires advanced hardware and knowledge in 3D rendering. With the rise of machine learning applications, the task became easier to solve for static objects, but there is still a lot of progress to be made…