3D pose estimation using convolutional neural networks
The present Master Thesis describes a new Pose Estimation method based on Convolutional Neural Networks (CNN). This method divides the three-dimensional space in several regions and, given an input image, returns the region where the camera is located. The first step is to create synthetic images of...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/85580 |
| Acceso en línea: | https://hdl.handle.net/2117/85580 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks (Computer science) Image processing Three-dimensional display systems Xarxes neuronals (Informàtica) Imatges -- Processament Visualització tridimensional (Informàtica) Àrees temàtiques de la UPC::Informàtica |
| Sumario: | The present Master Thesis describes a new Pose Estimation method based on Convolutional Neural Networks (CNN). This method divides the three-dimensional space in several regions and, given an input image, returns the region where the camera is located. The first step is to create synthetic images of the object simulating a camera located at di↵erent points around it. The CNN is pre-trained with these thousands of synthetic images of the object model. Then, we compute the pose of the object in hundreds of real images, and apply transfer learning with these labeled real images over the existing CNN, in order to refine the weights of the neurons and improve the network behaviour against real input images. Along with this deep learning approach, other techniques have been used trying to improve the quality of the results, such as the classical sliding window or a more recent class-generic object detector called objectness. It is tested with a 2D-model in order to ease the labeling process of the real images. This document outlines all the steps followed to create and test the method, and finally compares it against a state-of-the-art method at di↵erent scales and levels of blurring. |
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