384 research outputs found
Depth Enhancement and Surface Reconstruction with RGB/D Sequence
Surface reconstruction and 3D modeling is a challenging task, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. It is fundamental to many applications such as robot navigation, animation and scene understanding, industrial control and medical diagnosis. In this dissertation, I take advantage of the consumer depth sensors for surface reconstruction. Considering its limited performance on capturing detailed surface geometry, a depth enhancement approach is proposed in the first place to recovery small and rich geometric details with captured depth and color sequence. In addition to enhancing its spatial resolution, I present a hybrid camera to improve the temporal resolution of consumer depth sensor and propose an optimization framework to capture high speed motion and generate high speed depth streams. Given the partial scans from the depth sensor, we also develop a novel fusion approach to build up complete and watertight human models with a template guided registration method. Finally, the problem of surface reconstruction for non-Lambertian objects, on which the current depth sensor fails, is addressed by exploiting multi-view images captured with a hand-held color camera and we propose a visual hull based approach to recovery the 3D model
3D Reconstruction of Indoor Corridor Models Using Single Imagery and Video Sequences
In recent years, 3D indoor modeling has gained more attention due to its role in decision-making process of maintaining the status and managing the security of building indoor spaces. In this thesis, the problem of continuous indoor corridor space modeling has been tackled through two approaches. The first approach develops a modeling method based on middle-level perceptual organization. The second approach develops a visual Simultaneous Localisation and Mapping (SLAM) system with model-based loop closure.
In the first approach, the image space was searched for a corridor layout that can be converted into a geometrically accurate 3D model. Manhattan rule assumption was adopted, and indoor corridor layout hypotheses were generated through a random rule-based intersection of image physical line segments and virtual rays of orthogonal vanishing points. Volumetric reasoning, correspondences to physical edges, orientation map and geometric context of an image are all considered for scoring layout hypotheses. This approach provides physically plausible solutions while facing objects or occlusions in a corridor scene.
In the second approach, Layout SLAM is introduced. Layout SLAM performs camera localization while maps layout corners and normal point features in 3D space. Here, a new feature matching cost function was proposed considering both local and global context information. In addition, a rotation compensation variable makes Layout SLAM robust against cameras orientation errors accumulations. Moreover, layout model matching of keyframes insures accurate loop closures that prevent miss-association of newly visited landmarks to previously visited scene parts.
The comparison of generated single image-based 3D models to ground truth models showed that average ratio differences in widths, heights and lengths were 1.8%, 3.7% and 19.2% respectively. Moreover, Layout SLAM performed with the maximum absolute trajectory error of 2.4m in position and 8.2 degree in orientation for approximately 318m path on RAWSEEDS data set. Loop closing was strongly performed for Layout SLAM and provided 3D indoor corridor layouts with less than 1.05m displacement errors in length and less than 20cm in width and height for approximately 315m path on York University data set. The proposed methods can successfully generate 3D indoor corridor models compared to their major counterpart
Intelligent visual media processing: when graphics meets vision
The computer graphics and computer vision communities have been working closely together in recent
years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media
around us. There are three major driving forces behind this phenomenon: i) the availability of big data from the
Internet has created a demand for dealing with the ever increasing, vast amount of resources; ii) powerful processing
tools, such as deep neural networks, provide e�ective ways for learning how to deal with heterogeneous visual data;
iii) new data capture devices, such as the Kinect, bridge between algorithms for 2D image understanding and
3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics
and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey
recent research on how computer vision techniques bene�t computer graphics techniques and vice versa, and cover
research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest
possible further research directions
Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes
This report surveys advances in deep learning-based modeling techniques that
address four different 3D indoor scene analysis tasks, as well as synthesis of
3D indoor scenes. We describe different kinds of representations for indoor
scenes, various indoor scene datasets available for research in the
aforementioned areas, and discuss notable works employing machine learning
models for such scene modeling tasks based on these representations.
Specifically, we focus on the analysis and synthesis of 3D indoor scenes. With
respect to analysis, we focus on four basic scene understanding tasks -- 3D
object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene
similarity. And for synthesis, we mainly discuss neural scene synthesis works,
though also highlighting model-driven methods that allow for human-centric,
progressive scene synthesis. We identify the challenges involved in modeling
scenes for these tasks and the kind of machinery that needs to be developed to
adapt to the data representation, and the task setting in general. For each of
these tasks, we provide a comprehensive summary of the state-of-the-art works
across different axes such as the choice of data representation, backbone,
evaluation metric, input, output, etc., providing an organized review of the
literature. Towards the end, we discuss some interesting research directions
that have the potential to make a direct impact on the way users interact and
engage with these virtual scene models, making them an integral part of the
metaverse.Comment: Published in Computer Graphics Forum, Aug 202
A Comparative Study of Registration Methods for RGB-D Video of Static Scenes
The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.This work has been supported by a grant from the Spanish Government, DPI2013-40534-R, University of Alicante projects GRE11-01 and a grant from the Valencian Government, GV/2013/005
Structure from Motion with Higher-level Environment Representations
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
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