Melinos Averkiou

selected publications

1. 

   GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis

   Petrov, Dmitry, Goyal, Pradyumn, Thamizharasan, Vikas, Kim, Vladimir G., Gadelha, Matheus,  Averkiou, Melinos, Chaudhuri, Siddhartha, and Kalogerakis, Evangelos

   In Proc. SIGGRAPH, 2024

   Abs PDF Code Project Page

   We introduce GEM3D — a new deep, topology-aware generative model of 3D shapes. The key ingredient of our method is a neural skeleton-based representation encoding information on both shape topology and geometry. Through a denoising diffusion probabilistic model, our method first generates skeleton-based representations following the Medial Axis Transform (MAT), then generates surfaces through a skeleton-driven neural implicit formulation. The neural implicit takes into account the topological and geometric information stored in the generated skeleton representations to yield surfaces that are more topologically and geometrically accurate compared to previous neural field formulations. We discuss applications of our method in shape synthesis and point cloud reconstruction tasks, and evaluate our method both qualitatively and quantitatively. We demonstrate significantly more faithful surface reconstruction and diverse shape generation results compared to the state-of-the-art, also involving challenging scenarios of reconstructing and synthesizing structurally complex, high-genus shape surfaces from Thingi10K and ShapeNet.

2. 

   FacadeNet: Conditional Facade Synthesis via Selective Editing

   Georgiou, Yiangos, Loizou, Marios, Kelly, Tom, and Averkiou, Melinos

   In Proc. WACV, 2024

   Abs PDF Code Project Page

   We introduce FacadeNet, a deep learning approach for synthesizing building facade images from diverse viewpoints. Our method employs a conditional GAN, taking a single view of a facade along with the desired viewpoint information and generates an image of the facade from the distinct viewpoint. To precisely modify view-dependent elements like windows and doors while preserving the structure of view-independent components such as walls, we introduce a selective editing module. This module leverages image embeddings extracted from a pretrained vision transformer. Our experiments demonstrated state-of-the-art performance on building facade generation, surpassing alternative methods

3. 

   Cross-Shape Attention for Part Segmentation of 3D Point Clouds

   Loizou, M., Garg, S., Petrov, D.,  Averkiou, M., and Kalogerakis, E.

   Computer Graphics Forum (Proc. SGP), , 2023

   Abs PDF Code Project Page

   We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-shape attention mechanism to enable interactions between a shape’s point-wise features and those of other shapes. 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. Our method also proposes a shape retrieval measure to select suitable shapes for cross-shape attention operations for each test shape. Our experiments demonstrate that our approach yields state-of-the-art results in the popular PartNet dataset.

4. 

   An artificial neural network framework for classifying the style of cypriot hybrid examples of built heritage in 3D

   Artopoulos, G., Maslioukova, M. I., Zavou, C., Loizou, M., Deligiorgi, M., and Averkiou, M.

   Journal of Cultural Heritage, 63, 2023

   Abs PDF Project Page

   The article presents a workflow based on Deep Neural Networks (DNNs) and Support Vector Machine (SVM) for identifying architectural stylistic influences of segmented building parts of Cypriot historical architecture in 3D. The research contributes in the field of Digital Cultural Heritage (DCH) by applying Machine Learning (ML) and Deep Learning (DL) on recently published DCH data, with the aim to accelerate the segmentation and annotation process of Historic Building Information modelling (HBIM) that is currently based on time-consuming manual processes. The method presented works on reality captured data by 3D documentation techniques, precisely, Terrestrial Laser Scanning (TLS) or Photogrammetry. This workflow was developed to enable the operation of an online platform,11https://annfass-srv.cs.ucy.ac.cy. which also provides access to the building data presented here. Ultimately, the results of the presented method are accessible to scholars and students via this platform which provides multiple functionalities for researchers in the field to use.

5. 

   BuildingNet: Learning to Label 3D Buildings

   Selvaraju, P., Nabail, M., Loizou, M., Maslioukova, M.,  Averkiou, M., Andreou, A., Chaudhuri, S., and Kalogerakis, E.

   In Proc. ICCV, 2021

   Abs PDF Code Project Page Video

   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. Our project page www.buildingnet.org includes our dataset and code.

6. 

   3D Shape Segmentation with Projective Convolutional Networks

   Kalogerakis, Evangelos,  Averkiou, Melinos, Maji, Subhransu, and Chaudhuri, Siddhartha

   In Proc. CVPR, Jul 2017

   Abs PDF Code Project Page Video

   This paper introduces a deep architecture for segmenting 3D objects into their labeled semantic parts. Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are used for efficient view-based reasoning about 3D object parts. Through a special projection layer, FCN outputs are effectively aggregated across multiple views and scales, then are projected onto the 3D object surfaces. Finally, a surface-based CRF combines the projected outputs with geometric consistency cues to yield coherent segmentations. The whole architecture (multi-view FCNs and CRF) is trained end-to-end. Our approach significantly outperforms the existing state-of-the-art methods in the currently largest segmentation benchmark (ShapeNet). Finally, we demonstrate promising segmentation results on noisy 3D shapes acquired from consumer-grade depth cameras.