Computer vision is an important area focusing on understanding,
extracting and using the information from vision-based sensor. It
has many applications such as vision-based 3D reconstruction,
simultaneous localization and mapping(SLAM) and data-driven
understanding of the real world. Vision is a fundamental sensing
modality in many different fields of application.
While the traditional structure from motion mostly uses sparse
point-based feature, this thesis aims to explore the possibility
of using higher order feature representation. It starts with a
joint work which uses straight line for feature representation
and performs bundle adjustment with straight line
parameterization. Then, we further try an even higher order
representation where we use Bezier spline for parameterization.
We start with a simple case where all contours are lying on the
plane and uses Bezier splines to parametrize the curves in the
background and optimize on both camera position and Bezier
splines. For application, we present a complete end-to-end
pipeline which produces meaningful dense 3D models from natural
data of a 3D object: the target object is placed on a structured
but unknown planar background that is modeled with splines. The
data is captured using only a hand-held monocular camera.
However, this application is limited to a planar scenario and we
manage to push the parameterizations into real 3D. Following the
potential of this idea, we introduce a more flexible higher-order
extension of points that provide a general model for structural
edges in the environment, no matter if straight or curved. Our
model relies on linked B´ezier curves, the geometric intuition
of which proves great benefits during parameter initialization
and regularization. We present the
first fully automatic pipeline that is able to generate
spline-based representations without any human supervision.
Besides a full graphical formulation of the problem, we introduce
both geometric and photometric cues as well as higher-level
concepts such overall curve visibility and viewing angle
restrictions to automatically manage the correspondences in the
graph. Results prove that curve-based structure from motion with
splines is able to outperform state-of-the-art sparse
feature-based methods, as well as to model curved edges in the
environment