@inproceedings{PUT2021,

title = {Projective Urban Texturing},

author = {Yiangos Georgiou and Melinos Averkiou and Tom Kelly and Evangelos Kalogerakis},

year  = {2021},

date = {2021-12-01},

urldate = {2021-12-01},

booktitle = {International Conference on 3D Vision (3DV)},

abstract = {This paper proposes a method for automatic generation of textures for 3D city meshes in immersive urban environments. Many recent pipelines capture or synthesize large quantities of city geometry using scanners or procedural modeling pipelines. Such geometry is intricate and realistic, however the generation of photo-realistic textures for such large scenes remains a problem. We propose to generate textures for input target 3D meshes driven by the textural style present in readily available datasets of panoramic photos capturing urban environments. Re-targeting such 2D datasets to 3D geometry is challenging because the underlying shape, size, and layout of the urban structures in the photos do not correspond to the ones in the target meshes. Photos also often have objects (e.g., trees, vehicles) that may not even be present in the target geometry. To address these issues we present a method, called Projective Urban Texturing (PUT), which re-targets textural style from real-world panoramic images to unseen urban meshes. PUT relies on contrastive and adversarial training of a neural architecture designed for unpaired image-to-texture translation. The generated textures are stored in a texture atlas applied to the target 3D mesh geometry. To promote texture consistency, PUT employs an iterative procedure in which texture synthesis is conditioned on previously generated, adjacent textures. We demonstrate both quantitative and qualitative evaluation of the generated textures.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Selvaraju:2021:BuildingNet,

title = {BuildingNet: Learning to Label 3D Buildings},

author = {Pratheba Selvaraju and Mohamed Nabail and Marios Loizou and Maria Maslioukova and Melinos Averkiou and Andreas Andreou and Siddhartha Chaudhuri and Evangelos Kalogerakis},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {IEEE/CVF International Conference on Computer Vision (ICCV)},

abstract = {We introduce BuildingNet: (a) a large-scale dataset of 3D building models whose exteriors are consistently labeled, and (b) a graph neural network that labels building meshes by analyzing spatial and structural relations of their geometric primitives. To create our dataset, we used crowdsourcing combined with expert guidance, resulting in 513K annotated mesh primitives, grouped into 292K semantic part components across 2K building models. The dataset covers several building categories, such as houses, churches, skyscrapers, town halls, libraries, and castles. We include a benchmark for evaluating mesh and point cloud labeling. Buildings have more challenging structural complexity compared to objects in existing benchmarks (e.g., ShapeNet, PartNet), thus, we hope that our dataset can nurture the development of algorithms that are able to cope with such large-scale geometric data for both vision and graphics tasks e.g., 3D semantic segmentation, part-based generative models, correspondences, texturing, and analysis of point cloud data acquired from real-world buildings. Finally, we show that our mesh-based graph neural network significantly improves performance over several baselines for labeling 3D meshes.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}