Deformable 3-D Modelling from Uncalibrated Video Sequences
Year: 2013
Type: article
Abstract: The rigidity of a scene observed by a camera is often the fundamental assumption used to infer 3-D information automatically from the images taken by that camera. However, a video sequence of a natural scene often contains objects that modify their topology (for instance, a smiling face or a beating heart) thus violating the rigidity assumption necessary to reconstruct the 3-D structure of the object. In this thesis, we address the challenging problem of recovering the 3-D model of a deforming object and the motion of the camera observing it purely from image sequences, when nothing is known in advance about the observed object, the internal parameters of the camera or its motion. Previous solutions to this non-rigid structure from motion problem have either provided ap-proximate solutions using linear approaches to a problem that is intrinsically non-linear or re-quired strong assumptions about the nature of the 3-D deformations. In this thesis, we propose a non-linear framework based on bundle adjustment to estimate model and camera parameters. We then upgrade the proposed framework to deal with the case of a stereo camera setup. We show that when the deforming object is not performing a significant overall rigid motion a monocular
Author: Alessio Del Bue
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2139023354