no code implementations • 26 Feb 2024 • Dmitry Petrov, Pradyumn Goyal, Vikas Thamizharasan, Vladimir G. Kim, Matheus Gadelha, Melinos Averkiou, Siddhartha Chaudhuri, Evangelos Kalogerakis
We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes.
no code implementations • 2 Nov 2023 • Yiangos Georgiou, Marios Loizou, Tom Kelly, Melinos Averkiou
We introduce FacadeNet, a deep learning approach for synthesizing building facade images from diverse viewpoints.
1 code implementation • 5 Apr 2023 • Yeshwanth Kumar Adimoolam, Bodhiswatta Chatterjee, Charalambos Poullis, Melinos Averkiou
The CrowdAI Mapping Challenge Dataset is one of these datasets that has been used extensively in recent years to train deep neural networks.
no code implementations • 25 Jan 2022 • Yiangos Georgiou, Melinos Averkiou, Tom Kelly, Evangelos Kalogerakis
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.
1 code implementation • ICCV 2021 • Pratheba Selvaraju, Mohamed Nabail, Marios Loizou, Maria Maslioukova, Melinos Averkiou, Andreas Andreou, Siddhartha Chaudhuri, Evangelos Kalogerakis
We introduce BuildingNet: (a) a large-scale dataset of 3D building models whose exteriors are consistently labeled, (b) a graph neural network that labels building meshes by analyzing spatial and structural relations of their geometric primitives.
Ranked #1 on 3D Building Mesh Labeling on BuildingNet-Mesh
1 code implementation • 15 Jul 2020 • Marios Loizou, Melinos Averkiou, Evangelos Kalogerakis
We present a method that detects boundaries of parts in 3D shapes represented as point clouds.
1 code implementation • 20 Mar 2020 • Marios Loizou, Siddhant Garg, Dmitry Petrov, Melinos Averkiou, Evangelos Kalogerakis
The mechanism assesses both the degree of interaction between points and also mediates feature propagation across shapes, improving the accuracy and consistency of the resulting point-wise feature representations for shape segmentation.
Ranked #1 on 3D Semantic Segmentation on PartNet
no code implementations • 20 Oct 2018 • Hubert Lin, Melinos Averkiou, Evangelos Kalogerakis, Balazs Kovacs, Siddhant Ranade, Vladimir G. Kim, Siddhartha Chaudhuri, Kavita Bala
Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods.
1 code implementation • CVPR 2017 • Evangelos Kalogerakis, Melinos Averkiou, Subhransu Maji, Siddhartha Chaudhuri
Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes.