3d Reconstruction Of Indoor Scenes With A Rotating Camera
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3d Reconstruction Of Indoor Scenes With A Rotating Camera
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Author :
language : en
Publisher:
Release Date : 2013
3d Reconstruction Of Indoor Scenes With A Rotating Camera written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Multi Planar 3d Reconstruction Of Indoor Manhattan Scenes From Monocular Camera
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Author : Seongdo Kim
language : en
Publisher:
Release Date : 2018
Multi Planar 3d Reconstruction Of Indoor Manhattan Scenes From Monocular Camera written by Seongdo Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Three-dimensional (3D) reconstruction, a popular topic in computer vision, has been researched extensively for more than three decades. Many practitioners have proposed several image-based Structure-from-Motion (SfM) and visual Simultaneous Localization and Mapping (SLAM) algorithms to improve the quality, accuracy, and efficiency of 3D reconstruction results. Nevertheless, the 3D reconstruction of human-made indoor structures remains one of the most challenging problems since indoor environments present specific challenges due to their distinctive properties such as lack of textures and dramatic viewpoint changes.
3d Reconstruction Of Indoor Corridor Models Using Single Imagery And Video Sequences
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Author : Ali Baligh Jahromi
language : en
Publisher:
Release Date : 2019
3d Reconstruction Of Indoor Corridor Models Using Single Imagery And Video Sequences written by Ali Baligh Jahromi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
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.
Extrinsic Camera Parameter Acquisition For The 3 D Reconstruction Of Aerial Imagery
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Author : Michael J. Goldman
language : en
Publisher:
Release Date : 2008
Extrinsic Camera Parameter Acquisition For The 3 D Reconstruction Of Aerial Imagery written by Michael J. Goldman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
3d Reconstruction From Multiple Images
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Author : Theo Moons
language : en
Publisher: Now Publishers Inc
Release Date : 2009-10-23
3d Reconstruction From Multiple Images written by Theo Moons and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-23 with Computers categories.
The issue discusses methods to extract 3-dimensional (3D) models from plain images. In particular, the 3D information is obtained from images for which the camera parameters are unknown. The principles underlying such uncalibrated structure-from-motion methods are outlined. First, a short review of 3D acquisition technologies puts such methods in a wider context, and highlights their important advantages. Then, the actual theory behind this line of research is given. The authors have tried to keep the text maximally self-contained, therefore also avoiding to rely on an extensive knowledge of the projective concepts that usually appear in texts about self-calibration 3D methods. Rather, mathematical explanations that are more amenable to intuition are given. The explanation of the theory includes the stratification of reconstructions obtained from image pairs as well as metric reconstruction on the basis of more than 2 images combined with some additional knowledge about the cameras used. Readers who want to obtain more practical information about how to implement such uncalibrated structure-from-motion pipelines may be interested in two more Foundations and Trends issues written by the same authors. Together with this issue they can be read as a single tutorial on the subject.
Real Time Interactive 3d Reconstruction Of Indoor Environments With High Accuracy
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Author : Shakil Ahmed
language : en
Publisher:
Release Date : 2021
Real Time Interactive 3d Reconstruction Of Indoor Environments With High Accuracy written by Shakil Ahmed and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
3D registration of depth images has been studied extensively in the past. With emergence of low cost RGB-D cameras, many applications have emerged. Yet, the quality of alignment has much to improve. It remains a challenge to create a 3D model of the environment with high accuracy which could be used for engineering applications. Moreover, challenging scenarios where features are scarce, handling large areas as scene, enabling the user to freely roam around the scene for image acquisition in a practical manner, guiding the user in taking better images and accomplishing all of this with low cost RGB-D camera in real time is a subject which is yet to be improved. In this thesis, we address all this challenges by designing and implementing a mobile system that rely on markers. Our system detects and matches markers in real-time with very low CPU load. Moreover, this mobile 3D reconstruction system is interactive which enables a user not only to move freely while doing 3D reconstruction, but also makes the user aware of current status of 3D reconstruction in real-time.
Biomedical Engineering Systems And Technologies
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Author : Alberto Cliquet Jr.
language : en
Publisher: Springer
Release Date : 2019-08-12
Biomedical Engineering Systems And Technologies written by Alberto Cliquet Jr. and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-12 with Medical categories.
This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018, held in Funchal, Madeira, Portugal, in January 2018. The 25 revised full papers presented were carefully reviewed and selected from a total of 299 submissions. The papers are organized in topical sections on biomedical electronics and devices; bioimaging; bioinformatics models, methods and algorithms; health informatics.
3d Reconstruction And Camera Calibration From Circular Motion Image Sequences
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Author : Yan Li
language : en
Publisher: Open Dissertation Press
Release Date : 2017-01-27
3d Reconstruction And Camera Calibration From Circular Motion Image Sequences written by Yan Li and has been published by Open Dissertation Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-27 with categories.
This dissertation, "3D Reconstruction and Camera Calibration From Circular-motion Image Sequences" by Yan, Li, 李燕, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled "3D Reconstruction and Camera Calibration from Circular-Motion Image Sequences" Submitted by Li Yan for the degree of Doctor of Philosophy at The University of Hong Kong in December 2005 This thesis investigates the problem of 3D reconstruction from circular motion image sequences. The problem is normally resolved in two steps: projective reconstruction and then metric reconstruction by self-calibration. A key question considered in this thesis is how to make use of the circular motion information to improve the reconstruction accuracy and reduce the reconstruction ambiguity. The information is previously utilized by identifying the fixed image entities (e.g. the image of the rotation axis, vanishing line of the motion plane, etc). These fixed entities, however, only exist in constant intrinsic parameter sequences. In this thesis, circular motion constraints, which are valid for varying intrinsic parameter (e.g. zooming/refocusing) cameras, are formulated from the movement of camera center and principal plane. Based on the constraints, several novel algorithms are developed for each step of the whole 3D reconstruction procedure. For image sequences with known rotation angles, a circular projective reconstruction algorithm is proposed. We first formulate the circular motion constraints in the Euclidean frame, and then deduce the most general form of reconstruction in a projective frame that satisfies the circular motion constraints. The constraints are gradually enforced during an iterative process, resulting in a circular projective reconstruction. This approach can be used to deal with both cases of constant and varying intrinsic parameters. It is proved that the circular projective reconstruction retrieves metric reconstruction up to a two-parameter ambiguity representing a projective distortion along the rotation axis of the circular motion. Based on the circular projective reconstruction, a hierarchical self-calibration algorithm is proposed to estimate the remaining two parameters. Closed-form expressions of the absolute conic and its image are deduced in terms of the two parameters, which are then determined with zero-skew and unit aspect ratio assumptions. Alternatively, starting from a general (rather than circular) projective reconstruction, a stratified self-calibration algorithm is proposed to upgrade the projective reconstruction directly to a metric one. In this case, the plane at infinity is first identified with (i) the circular motion constraint on camera center and (ii) zero-skew and unit aspect ratio assumptions. With the knowledge of the plane at infinity, the camera calibration matrices can be readily obtained. All the above algorithms assume that the rotation angles are known. In the case if the angles are unknown, we provide two novel rotation angle recovery methods. For constant intrinsic parameter sequences, rotation angles can be recovered by using the fixed image entities. For varying intrinsic parameter sequences, it is shown that the movements of the camera center and principal plane form two concentric circles on the motion plane. By identifying the corresponding conic loci in 3D projective frame, the geometry of circular motion on the motion plane can be recovered. Compared with existing methods, the new method is more flexible in that it allows the intrinsic parameters to vary, and is simpler by avoi
Towards Photo Realistic 3d Reconstruction From Casual Scanning
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Author : Jeong Joon Park
language : en
Publisher:
Release Date : 2021
Towards Photo Realistic 3d Reconstruction From Casual Scanning written by Jeong Joon Park and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
In this thesis, I address the problem of obtaining photo-realistic 3D models of small-scale indoor scenes from a stream of images captured with a hand-held camera. Recovering 3D structure of real-world scenes has been an important topic of research in computer vision, due to its wide applicability in virtual tourism, augmented reality, autonomous-driving or robotics. While numerous reconstruction methods have been proposed, they typically present trade-offs between practicality of capture and the realism of the reconstructed model. I introduce novel 3D reconstruction techniques that effectively navigate the trade-off curve, in order to produce photo-realistic models from user-friendly capture setups. Finally, I suggest new directions for learning generalizable scene priors to enable capture from partial inputs. Creating a photo-realistic digital replica of a physical scene involves careful modeling of geometry, surface materials, and scene lighting, all of which I address in this thesis. At the same time, a reconstruction system should be easy to use for casual users to truly unlock 3D-related applications. This thesis suggests three criteria required for a casual reconstruction system that could greatly reduce the time and resources during scanning: i) the input method should be from a hand-held consumer-grade camera, ii) the system should reconstruct full appearance from a handful of input views of a scene as opposed to a dense view-sampling, and iii) it should automatically complete unobserved parts of a scene. The thesis proposes novel techniques to tackle each of these criteria. I first describe a technique to reconstruct the appearance of shiny objects, leveraging the infrared laser system of an RGB-D sensor as a calibrated point light source to recover surface reflectance. This method takes video as an input from a hand-held camera and the scene lighting captured with a 360$^\circ$ camera to generate a realistic replication of a scene, featuring high-resolution texture and specular highlight modeling. The output model allows virtually rendering the captured scene from any viewing direction. Next, I discuss joint reconstruction of photo-realistic scene appearance and environment lighting of a target scene using a hand-held sensor. I achieve this through a joint optimization of a segmentation neural network, and a material-specific lighting model to reconstruct the input images, and adopt a neural network-enhanced rendering technique that achieve exceptional realism. The combination of physics and machine learning achieves both photo-realism and the ability to extrapolate to new views, reducing the range of required views by the users. While the first two approaches allow realistic reconstruction from casual scanning, they can only model surfaces that are captured during scanning, i.e., they do not complete missing surfaces. Completing unobserved regions typically calls for machine learning algorithms to extract and apply scene/object priors from a large database. Traditionally, the lack of efficient 3D representations has limited the development of deep learning approaches in 3D. To facilitate machine learning in 3D, I devise my DeepSDF approach that describes 3D surface as a decision boundary of a neural network, which is highly efficient in memory and at the same time can model continuous surfaces. The new representation, along with a newly proposed learning algorithm, allows reconstructing a full, plausible shape from a partial and noisy object scan. I show through experiments that the new representation is highly effective in learning geometric priors from a dataset of objects. Finally, I extend the DeepSDF representation to model multi-object scenes. Specifically, I introduce a new method of training a generative model of unaligned objects via an adversarial training in the feature space. I show that reconstructing a multi-object scene from a noisy, partial scan amounts to simply optimizing the randomly initialized latent vectors of the generative model to fit the observed points.
Making 3d Point Cloud Reconstruction Computationally Efficient
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Author : Chih-Hsiang Chang
language : en
Publisher:
Release Date : 2016
Making 3d Point Cloud Reconstruction Computationally Efficient written by Chih-Hsiang Chang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Computational photography categories.
This dissertation covers the problem of 3D reconstruction of a scene from a number of images taken from that scene. Although this problem has been previously addressed in the computer vision literature, what differentiates this dissertation research from what has already been done is the computational efficiency aspect of various computer vision modules. The contributions made in this dissertation to the computational efficiency aspect of 3D reconstruction are published or submitted as five papers that appear as five chapters in this dissertation. Each chapter provides an abstract of the contribution made, an introduction and literature review, the methodology developed, the results obtained together with their discussions, and conclusion associated with that contribution. Chapter 1 targets the bundle adjustment module of 3D reconstruction where it is shown how a local bundle adjustment approach improves the computational complexity. In Chapter 2, a two- stage scheme is developed for camera pose estimation. The main advantage of this scheme is that the computation burden caused by the Levenberg-Marquardt optimization is avoided. Chapter 3 targets the frame selection and camera rotation registration aspects of 3D reconstruction. Initially, the translation vector is estimated by using the relative camera pose and 3D correspondences. Then, a rotation registration is considered to generate the camera rotation matrix. The developed approach reduces the re-projection error in each frame at a lower computational complexity compared to the conventional approach. Chapter 4 targets the computational efficiency aspect of the entire 3D reconstruction pipeline by providing a new absolute camera pose recovery approach in a computationally efficient manner. The experimental results show the developed pipeline generates lower re-projection errors and higher frame rates towards 3D reconstruction. Finally, in Chapter 5, the camera pose estimation is applied to the problem of vanishing point detection for camera orientation applications. A fast J-linkage algorithm is developed to perform vanishing point detection. Then, this algorithm is used to recover the camera rotation in a computationally efficient manner. The contributions presented in the chapters offer a computationally efficient framework towards making 3D reconstruction from video sequences feasible on laptop devices.