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Neural Scene Decomposition for Multi-Person Motion Capture

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Real-Time Seamless Single Shot 6D Object Pose Prediction

Pascal Fua, Bugra Tekin, Sudipta Sinha

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [ ...

2018

Real-Time Seamless Single Shot 6D Object Pose Prediction

Pascal Fua, Bugra Tekin, Sudipta Sinha

IEEE2018

SGAN: An Alternative Training of Generative Adversarial Networks

François Fleuret, Tatjana Chavdarova

The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results ...

IEEE2018

HeadFusion: 360 degrees Head Pose Tracking Combining 3D Morphable Model and 3D Reconstruction

Jean-Marc Odobez, Kenneth Alberto Funes Mora, Yu Yu

Head pose estimation is a fundamental task for face and social related research. Although 3D morphable model (3DMM) based methods relying on depth information usually achieve accurate results, they usually require frontal or mid-profile poses which preclud ...

IEEE COMPUTER SOC2018

3D Box Proposals from a Single Monocular Image of an Indoor Scene

Mathieu Salzmann, Wei Zhuo

Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume h ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Learning Aerial Image Segmentation from Online Maps

Martin Jaggi, Jan Dirk Wegner, Aurélien Lucchi, Thomas Hofmann

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) ...

Institute of Electrical and Electronics Engineers2017

Vision-based detection of aircrafts and UAVs

Artem Rozantsev

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

EPFL2017

Graph-based Isometry Invariant Representation Learning

Pascal Frossard, Renata Khasanova

Learning transformation invariant representations of visual data is an important problem in computer vision. Deep convolutional networks have demonstrated remarkable results for image and video classification tasks. However, they have achieved only limited ...

2017

Machine Vision algorithms on cadaster plans

Sofia Ares Oliveira

Cadaster plans are cornerstones for reconstructing dense representations of the history of the city. However, as some of these handwritten documents are more than 200 years old, the establishment of processing pipeline for interpreting them remains extreme ...

2017

Modern techniques and applications for real-time non-rigid registration

Mark Pauly, Hao Li, Sofien Bouaziz, Andrea Tagliasacchi

Registration algorithms are an essential component of many computer graphics and computer vision systems. With recent technological advances in RGBD sensors (color plus depth), an active area of research is in techniques combining color, geometry, and lear ...

2016