Search Results for author: Evangelos Kalogerakis

Found 24 papers, 15 papers with code

BuildingNet: Learning to Label 3D Buildings

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.

3D Building Mesh Labeling 3D Semantic Segmentation

Neural Strokes: Stylized Line Drawing of 3D Shapes

1 code implementation ICCV 2021 Difan Liu, Matthew Fisher, Aaron Hertzmann, Evangelos Kalogerakis

We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours.

Learning Part Boundaries from 3D Point Clouds

1 code implementation15 Jul 2020 Marios Loizou, Melinos Averkiou, Evangelos Kalogerakis

We present a method that detects boundaries of parts in 3D shapes represented as point clouds.

RigNet: Neural Rigging for Articulated Characters

1 code implementation1 May 2020 Zhan Xu, Yang Zhou, Evangelos Kalogerakis, Chris Landreth, Karan Singh

We present RigNet, an end-to-end automated method for producing animation rigs from input character models.

MakeItTalk: Speaker-Aware Talking-Head Animation

2 code implementations27 Apr 2020 Yang Zhou, Xintong Han, Eli Shechtman, Jose Echevarria, Evangelos Kalogerakis, DIngzeyu Li

We present a method that generates expressive talking heads from a single facial image with audio as the only input.

ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

2 code implementations ECCV 2020 Gopal Sharma, Difan Liu, Subhransu Maji, Evangelos Kalogerakis, Siddhartha Chaudhuri, Radomír Měch

We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives.

Cross-Shape Graph Convolutional Networks

no code implementations20 Mar 2020 Dmitry Petrov, Evangelos Kalogerakis

Our method also learns to create a graph over shapes of an input collection whose edges connect shapes deemed as useful for performing cross-shape convolution.

Semantic Segmentation

Neural Shape Parsers for Constructive Solid Geometry

no code implementations22 Dec 2019 Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji

We investigate two architectures for this task --- a vanilla encoder (CNN) - decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack.

Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation

no code implementations3 Oct 2019 Gopal Sharma, Evangelos Kalogerakis, Subhransu Maji

We present a framework for learning representations of 3D shapes that reflect the information present in this meta data and show that it leads to improved generalization for semantic segmentation tasks.

Metric Learning Semantic Segmentation

Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets

no code implementations22 Aug 2019 Zhan Xu, Yang Zhou, Evangelos Kalogerakis, Karan Singh

We present a learning method for predicting animation skeletons for input 3D models of articulated characters.

SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation

2 code implementations ICCV 2019 Yang Zhou, Zachary While, Evangelos Kalogerakis

In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings.

3D Object Recognition Scene Generation

Learning Material-Aware Local Descriptors for 3D Shapes

no code implementations20 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.

Material Classification

Deep Part Induction from Articulated Object Pairs

1 code implementation19 Sep 2018 Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, Leonidas Guibas

In this paper, we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects.

VisemeNet: Audio-Driven Animator-Centric Speech Animation

no code implementations24 May 2018 Yang Zhou, Zhan Xu, Chris Landreth, Evangelos Kalogerakis, Subhransu Maji, Karan Singh

We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio.


SPLATNet: Sparse Lattice Networks for Point Cloud Processing

2 code implementations CVPR 2018 Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz

We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.

3D Part Segmentation 3D Semantic Segmentation

CSGNet: Neural Shape Parser for Constructive Solid Geometry

1 code implementation CVPR 2018 Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji

In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions.

High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

no code implementations ICCV 2017 Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, Yizhou Yu

Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network.

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

3 code implementations20 Jul 2017 Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis, Subhransu Maji, Rui Wang

The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints.

3D Reconstruction 3D Shape Reconstruction

Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks

no code implementations14 Jun 2017 Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri, Duygu Ceylan, Vladimir G. Kim, Ersin Yumer

We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance prediction, and shape-to-scan matching.

Semantic Segmentation

3D Shape Segmentation with Projective Convolutional Networks

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.

Multi-view Convolutional Neural Networks for 3D Shape Recognition

no code implementations ICCV 2015 Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller

A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors?

3D Point Cloud Classification 3D Shape Recognition

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