Publications associées à Calibration de caméra

Gravity as a Reference for Estimating a Person's Height from Video

Pascal Fua, Helge Jochen Rhodin, Semih Günel, Didier Bieler

Estimating the metric height of a person from monocular imagery without additional assumptions is ill-posed. Existing solutions either require manual calibration of ground plane and camera geometry, special cameras, or reference objects of known size. We f ...

IEEE2019

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

Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

Achieving robust multi-person 2D body landmark localization and pose estimation is essential for human behavior and interaction understanding as encountered for instance in HRI settings. Accurate methods have been proposed recently, but they usually rely o ...

2019

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Mert Yüksekgönül

Deep neural networks are highly effective tools for human and animal pose estimation. However, robustness to out-of-domain data remains a challenge. Here, we probe the transfer and generalization ability for pose estimation with two architecture classes (M ...

2019

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny, Yuki Ono

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...

2018

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny, Yuki Ono

IEEE2018

Achieving robotically peeled lettuce

Josephine Anna Eleanor Hughes

Robotic technologies are being increasingly applied to agriculture, in particular to harvesting. Some types of produce such as iceberg lettuce require additional processing after harvesting in order to satisfy the needs of the end-user or customer. Lettuce ...

Institute of Electrical and Electronics Engineers Inc.2018

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

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

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Calibration de caméra

Gravity as a Reference for Estimating a Person's Height from Video

Neural Scene Decomposition for Multi-Person Motion Capture

Robot Identification and Localization with Pointing Gestures

Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation

Pretraining boosts out-of-domain robustness for pose estimation

Learning to Find Good Correspondences

Learning to Find Good Correspondences

Achieving robotically peeled lettuce

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

Real-Time Seamless Single Shot 6D Object Pose Prediction

Graph Chatbot

Chattez avec Graph Search

Neural Scene Decomposition for Multi-Person Motion Capture

Learning to Find Good Correspondences

Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation

Gravity as a Reference for Estimating a Person's Height from Video

Robot Identification and Localization with Pointing Gestures

Achieving robotically peeled lettuce

Pretraining boosts out-of-domain robustness for pose estimation

Real-Time Seamless Single Shot 6D Object Pose Prediction

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

Learning to Find Good Correspondences