Dense 3d Point Cloud Representation Of A Scene Using Uncalibrated Monocular Vision
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Dense 3d Point Cloud Representation Of A Scene Using Uncalibrated Monocular Vision
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Author : Yakov Diskin
language : en
Publisher:
Release Date : 2013
Dense 3d Point Cloud Representation Of A Scene Using Uncalibrated Monocular Vision written by Yakov Diskin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computer vision categories.
We present a 3D reconstruction algorithm designed to support various automation and navigation applications. The algorithm presented focuses on the 3D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point cloud model of its unknown surroundings. In this thesis, we present the step by step methodology of the development of a reconstruction technique. The original reconstruction process, resulting with a point cloud was computed based on feature matching and depth triangulation analysis. In an improved version of the algorithm, we utilized optical flow features to create an extremely dense representation model. Although dense, this model is hindered due to its low disparity resolution. As feature points were matched from frame to frame, the resolution of the input images and the discrete nature of disparities limited the depth computations within a scene. With the third algorithmic modification, we introduce the addition of the preprocessing step of nonlinear super resolution. With this addition, the accuracy of the point cloud which relies on precise disparity measurement has significantly increased. Using a pixel by pixel approach, the super resolution technique computes the phase congruency of each pixel's neighborhood and produces nonlinearly interpolated high resolution input frames. Thus, a feature point travels a more precise discrete disparity. Also, the quantity of points within the 3D point cloud model is significantly increased since the number of features is directly proportional to the resolution and high frequencies of the input image. Our final contribution of additional preprocessing steps is designed to filter noise points and mismatched features, giving birth to the complete Dense Point-cloud Representation (DPR) technique. We measure the success of DPR by evaluating the visual appeal, density, accuracy and computational expense of the reconstruction technique and compare with two state-of-the-arts techniques. After the presentation of rigorous analysis and comparison, we conclude by presenting the future direction of development and its plans for deployment in real-world applications.
Fastslam
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Author : Michael Montemerlo
language : en
Publisher: Springer
Release Date : 2007-04-27
Fastslam written by Michael Montemerlo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-27 with Technology & Engineering categories.
This monograph describes a new family of algorithms for the simultaneous localization and mapping (SLAM) problem in robotics, called FastSLAM. The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy, in a number of robot application domains and have been successfully applied in different dynamic environments, including a solution to the problem of people tracking.
A Rational Finite Element Basis
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Author : Wachspress
language : en
Publisher: Academic Press
Release Date : 1975-09-26
A Rational Finite Element Basis written by Wachspress and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975-09-26 with Computers categories.
A Rational Finite Element Basis
Towards Visual Inertial Slam For Mobile Augmented Reality
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Author : Gabriele Bleser
language : en
Publisher:
Release Date : 2009
Towards Visual Inertial Slam For Mobile Augmented Reality written by Gabriele Bleser and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Erweiterte Realität Informatik - Echtzeitbildverarbeitung - Kamera - Zielverfolgung - Merkmalsextraktion - Registrierung Bildverarbeitung categories.
Robust Methods For Dense Monocular Non Rigid 3d Reconstruction And Alignment Of Point Clouds
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Author : Vladislav Golyanik
language : en
Publisher: Springer Nature
Release Date : 2020-06-04
Robust Methods For Dense Monocular Non Rigid 3d Reconstruction And Alignment Of Point Clouds written by Vladislav Golyanik and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-04 with Computers categories.
Vladislav Golyanik proposes several new methods for dense non-rigid structure from motion (NRSfM) as well as alignment of point clouds. The introduced methods improve the state of the art in various aspects, i.e. in the ability to handle inaccurate point tracks and 3D data with contaminations. NRSfM with shape priors obtained on-the-fly from several unoccluded frames of the sequence and the new gravitational class of methods for point set alignment represent the primary contributions of this book. About the Author: Vladislav Golyanik is currently a postdoctoral researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. The current focus of his research lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of human body and matching problems on point sets and graphs. He is interested in machine learning (both supervised and unsupervised), physics-based methods as well as new hardware and sensors for computer vision and graphics (e.g., quantum computers and event cameras).
Deep Learning On Point Clouds For 3d Scene Understanding
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Author : Ruizhongtai Qi
language : en
Publisher:
Release Date : 2018
Deep Learning On Point Clouds For 3d Scene Understanding written by Ruizhongtai Qi 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.
Point cloud is a commonly used geometric data type with many applications in computer vision, computer graphics and robotics. The availability of inexpensive 3D sensors has made point cloud data widely available and the current interest in self-driving vehicles has highlighted the importance of reliable and efficient point cloud processing. Due to its irregular format, however, current convolutional deep learning methods cannot be directly used with point clouds. Most researchers transform such data to regular 3D voxel grids or collections of images, which renders data unnecessarily voluminous and causes quantization and other issues. In this thesis, we present novel types of neural networks (PointNet and PointNet++) that directly consume point clouds, in ways that respect the permutation invariance of points in the input. Our network provides a unified architecture for applications ranging from object classification and part segmentation to semantic scene parsing, while being efficient and robust against various input perturbations and data corruption. We provide a theoretical analysis of our approach, showing that our network can approximate any set function that is continuous, and explain its robustness. In PointNet++, we further exploit local contexts in point clouds, investigate the challenge of non-uniform sampling density in common 3D scans, and design new layers that learn to adapt to varying sampling densities. The proposed architectures have opened doors to new 3D-centric approaches to scene understanding. We show how we can adapt and apply PointNets to two important perception problems in robotics: 3D object detection and 3D scene flow estimation. In 3D object detection, we propose a new frustum-based detection framework that achieves 3D instance segmentation and 3D amodal box estimation in point clouds. Our model, called Frustum PointNets, benefits from accurate geometry provided by 3D points and is able to canonicalize the learning problem by applying both non-parametric and data-driven geometric transformations on the inputs. Evaluated on large-scale indoor and outdoor datasets, our real-time detector significantly advances state of the art. In scene flow estimation, we propose a new deep network called FlowNet3D that learns to recover 3D motion flow from two frames of point clouds. Compared with previous work that focuses on 2D representations and optimizes for optical flow, our model directly optimizes 3D scene flow and shows great advantages in evaluations on real LiDAR scans. As point clouds are prevalent, our architectures are not restricted to the above two applications or even 3D scene understanding. This thesis concludes with a discussion on other potential application domains and directions for future research.
Reconstruction And Analysis Of 3d Scenes
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Author : Martin Weinmann
language : en
Publisher: Springer
Release Date : 2016-03-17
Reconstruction And Analysis Of 3d Scenes written by Martin Weinmann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-17 with Computers categories.
This unique work presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced, incorporating each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: reviews techniques for the acquisition of 3D point cloud data and for point quality assessment; explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data; proposes an original approach to keypoint-based point cloud registration; discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping; presents a novel framework for 3D scene analysis.
3d Point Cloud Unsupervised Representation Learning By Indoor Scene Context Reconstruction
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Author : 劉岳承
language : en
Publisher:
Release Date : 2020
3d Point Cloud Unsupervised Representation Learning By Indoor Scene Context Reconstruction written by 劉岳承 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.