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Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

Publications associées (55)

Structure-aware Multi-view 3D Reconstruction of Dislocations in TEM with Message Passing Neural Networks

Pascal Fua, Cécile Hébert, Emad Oveisi, Gulnaz Ganeeva, Anastasiia Mishchuk, Okan Altingövde

Efficient analysis of the three-dimensional (3D) shape and distribution of curvilinear crystal defects, namely dislocations, is an open research topic in material science and computer vision. In order to determine the structural and opto-electrical charact ...
2021

Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans

Amir Roshan Zamir, Alexander Evan Sax, Ainaz Eftekhar

This paper introduces a pipeline to parametrically sample and render static multi-task vision datasets from comprehensive 3D scans from the real-world. In addition to enabling interesting lines of research, we show the tooling and generated data suffice to ...
IEEE2021

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Fernando Perez Cruz

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss ...
2021

Learning stereo reconstruction with deep neural networks

Stepan Tulyakov

Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...
EPFL2020

Beyond Cameras: Real-Time High-Resolution 3D and Panoramic MultiView Vision Systems with Their Applications

Bilal Demir

The cameras are invented by imitating the human visual system to capture the scene. The camera technologies have been substantially advanced in recent years. 108 MP resolution with 100x hybrid zoom has become standard features for smartphone flagships. In ...
EPFL2020

Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity

Rémi Guillaume Petitpierre

In this work, we present a new semantic segmentation model for historical city maps that surpasses the state of the art in terms of flexibility and performance. Research in automatic map processing is largely focused on homogeneous corpora or even individu ...
2020

Divergence-Based Adaptive Extreme Video Completion

Sabine Süsstrunk, Fabrice Jean Guibert, Majed El Helou, Ruofan Zhou, Frank Schmutz

Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans an ...
2020

Neural Scene Decomposition for Multi-Person Motion Capture

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Helge Jochen Rhodin, Victor Constantin

Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networks that were initially trained on ImageNet, mostly because the learned featu ...
IEEE2019

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...
EPFL2019

Deep neural networks for plasma tomography with applications to JET and COMPASS

Olivier Sauter, Yiming Li, Ambrogio Fasoli, Basil Duval, Jonathan Graves, Duccio Testa, Patrick Blanchard, Alessandro Pau, Federico Alberto Alfredo Felici, Cristian Sommariva, Antoine Pierre Emmanuel Alexis Merle, Haomin Sun, Michele Marin, Henri Weisen, Richard Pitts, Yann Camenen, Jan Horacek, Javier García Hernández, Marco Wischmeier, Nicola Vianello, Mikhail Maslov, Federico Nespoli, Yao Zhou, Davide Galassi, Antonio José Pereira de Figueiredo, Hamish William Patten, Samuel Lanthaler, Emiliano Fable, Francesca Maria Poli, Daniele Brunetti, Anna Teplukhina, Alberto Mariani, Kenji Tanaka, Bernhard Sieglin, Otto Asunta, Gergely Papp, Leonardo Pigatto

Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural network ...
2019

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