@unpublished {xRamon, title = {Deep Learning algorithms for 3D Reconstruction and Simulation of Aesthetic Procedures}, year = {2018}, month = {07/2018}, type = {Phd Thesis Proposal}, abstract = {

3D technology is key for a wide range of industries. Medicine, construction, cinema and many other disciplines can nowadays digitalize the world we perceive using 3D reconstruction algorithms, create new objects by means of 3D printers or analyze the world using 3D detection and segmentation techniques.

These tools are transforming how research and industry problems are addressed. Concretely, in the field of aesthetic surgery, a fluent communication between doctors and patients is crucial in order to maximize the success of the operatories and the satisfaction of the patients. A new trend in the industry is to incorporate 3D technology during the consultation, with the goa lof improving that communication. By reconstructing the body of the patient and simulating aesthetic procedures on it, he or she can realise how a concrete modification would look like when applied to his or her body. It is also beneficial for the physicians, since they can measure the effectiveness of the applied treatments and also convert more consultations into operations due to an increase of confidence in the patient.

This thesis proposal defines the research directions to follow during an industrial doctorate to be developed in Crisalix Labs, in collaboration of the Image Processing Group at the Universitat Polit{\`e}cnica de Catalunya. Industrial doctorates are promoted from the Government of Catalonia to promote the transfer of knowledge from universities to local industry as an element for innovation and technical excellence.

}, author = {Ramon, Eduard}, editor = {Xavier Gir{\'o}-i-Nieto} }