@conference {AbuAlhaija2019,
	title = {Geometric Image Synthesis},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11366 LNCS},
	year = {2019},
	pages = {85{\textendash}100},
	abstract = {The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation, to create realistic looking natural images that match the desired scene structure. Our geometrically-consistent image synthesis method is a deep neural network, called Geometry to Image Synthesis (GIS) framework, which retains the advantages of a trainable method, e.g., differentiability and adaptiveness, but, at the same time, makes a step towards the generalizability, control and quality output of modern graphics rendering engines. We utilize the GIS framework to insert vehicles in outdoor driving scenes, as well as to generate novel views of objects from the Linemod dataset. We qualitatively show that our network is able to generalize beyond the training set to novel scene geometries, object shapes and segmentations. Furthermore, we quantitatively show that the GIS framework can be used to synthesize large amounts of training data which proves beneficial for training instance segmentation models.},
	isbn = {9783030208752},
	issn = {16113349},
	doi = {10.1007/978-3-030-20876-9_6},
	url = {https://youtu.be/W2tFCz9xJoU},
	author = {Hassan Abu Alhaija and Mustikovela, Siva Karthik and Geiger, Andreas and Carsten Rother}
}
@proceedings {6309,
	title = {Geometric Image Synthesis},
	year = {2018},
	author = {Hassan Abu Alhaija and Mustikovela, S K and Geiger, A and Carsten Rother}
}
@conference {Alhaija2015,
	title = {Graphflow{\textemdash}6D large displacement scene flow via graph matching},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9358},
	year = {2015},
	pages = {285{\textendash}296},
	abstract = {We present an approach for computing dense scene flow from two large displacement RGB-D images. When dealing with large displacements the crucial step is to estimate the overall motion correctly. While state-of-the-art approaches focus on RGB information to establish guiding correspondences, we explore the power of depth edges. To achieve this, we present a new graph matching technique that brings sparse depth edges into correspondence. An additional contribution is the formulation of a continuous-label energy which is used to densify the sparse graph matching output. We present results on challenging Kinect images, for which we outperform state-of-the-art techniques.},
	isbn = {9783319249469},
	issn = {16113349},
	doi = {10.1007/978-3-319-24947-6_23},
	author = {Hassan Abu Alhaija and Sellent, Anita and Kondermann, Daniel and Carsten Rother}
}
