3D Object Model Reconstruction with Texture Mapping Using RGB-D Camera PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download 3D Object Model Reconstruction with Texture Mapping Using RGB-D Camera PDF full book. Access full book title 3D Object Model Reconstruction with Texture Mapping Using RGB-D Camera by Yong-Sheng Chen. Download full books in PDF and EPUB format.
Author: Yong-Sheng Chen
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Yong-Sheng Chen
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Mayoore Selvarasa Jaiswal
Publisher:
ISBN:
Category :
Languages : en
Pages : 123
Get Book Here
Book Description
With the introduction of economical depth cameras, computer vision research has made a huge leap forward in 3D reconstruction and understanding. However, the quality of the depth images are limited: 1) depth images contain holes and random noise due to the characteristics of the cameras and the physical world, and 2) depth images have a lower spatial resolution. These challenges make 3D object reconstruction with a limited number of RGB-D frames a difficult task. In this dissertation, we propose a method to construct 3D object models with a limited number of RGB and depth frames. We developed a complete 3D object model construction process with automatic object segmentation, pairwise registration, global alignment, model denoising, and texturing, and studied the effects of these functions on the constructed 3D object models. We also developed a process for objective performance evaluation of the constructed 3D object models. High-quality depth images are paramount to create quality 3D object models. Many depth denoising methods blur object boundaries. Partial point clouds created from depth images denoised by these methods have artifacts that make them unsuitable for 3D object modeling. We propose a method to find the clean depth edge image using the noisy depth image and a color image, then use this clean depth edge image with the proposed edge and color aware adaptive trilateral filter to obtain denoised depth images. Finally, we propose to apply depth denoising and depth super-resolution in the 3D object modeling process. We preprocess depth images with the proposed depth denoising method and a state-of-the-art depth super-resolution method, then experiment the use of these preprocessed depth images under various conditions in the 3D object modeling process. We show that depth denoising and super-resolution can improve the quality of the 3D object models.
Author: Pitzer, Benjamin
Publisher: KIT Scientific Publishing
ISBN: 3866448058
Category : Technology (General)
Languages : en
Pages : 184
Get Book Here
Book Description
Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models.
Author: Chao Wang (Computer graphics engineer)
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages :
Get Book Here
Book Description
3D Reconstruction is one of the most popular topics in the area of computer graphics and vision. A typical 3D reconstruction process is to reconstruct the 3D model or other similar geometry representations from different sources of data, including color images and depth data captured by depth cameras. Online and offline RGB-D (RGB and depth) reconstruction techniques have been developing rapidly in this decade with the prevalence of consumer depth cameras. However, current 3D construction methods still lack robustness in the tracking process, and also pay little attention on the quality of final reconstructed 3D models. This dissertation is focused on improving the robustness of camera tracking in the online RGB-D reconstruction process, as well as optimizing camera pose, face texture and geometry quality of 3D models in the offline RGB-D reconstruction with consumer depth cameras. One problem in online 3D reconstruction is that, existing camera pose estimation approaches in online RGB-D reconstruction systems always suffer from fast-scanned data and generate inaccurate relative transformations between consecutive frames. In order to improve the tracking robustness of online 3D reconstruction, we propose a novel feature-based camera pose optimization algorithm for real-time 3D reconstruction systems. We have demonstrated that our method improves current methods by utilizing matched features across all frames, and is robust on reconstructing RGB-D data with large adjacent shifts across frames. Another problem in RGB-D reconstruction is that the geometry of reconstructed 3D models is usually too dense and coarse, and texture quality of mesh faces is always too low. To deal with this problem, we introduce a new plane-based RGB-D reconstruction approach with plane, geometry and texture optimization to improve the geometry and texture quality of reconstructed models. Compared to existing planar reconstruction methods which cover only large planar regions in the scene, our method reconstructs the entire original scene without losing geometry details in the low-polygonal lightweight result meshes with clear face textures and sharp features. We have demonstrated the effectiveness of our approach by applying it onto different RGB-D benchmarks and comparing it with other state-of-the-art reconstruction methods.
Author: Yonghuai Liu
Publisher: Springer Nature
ISBN: 3030440702
Category : Computers
Languages : en
Pages : 736
Get Book Here
Book Description
This textbook is designed for postgraduate studies in the field of 3D Computer Vision. It also provides a useful reference for industrial practitioners; for example, in the areas of 3D data capture, computer-aided geometric modelling and industrial quality assurance. This second edition is a significant upgrade of existing topics with novel findings. Additionally, it has new material covering consumer-grade RGB-D cameras, 3D morphable models, deep learning on 3D datasets, as well as new applications in the 3D digitization of cultural heritage and the 3D phenotyping of crops. Overall, the book covers three main areas: ● 3D imaging, including passive 3D imaging, active triangulation 3D imaging, active time-of-flight 3D imaging, consumer RGB-D cameras, and 3D data representation and visualisation; ● 3D shape analysis, including local descriptors, registration, matching, 3D morphable models, and deep learning on 3D datasets; and ● 3D applications, including 3D face recognition, cultural heritage and 3D phenotyping of plants. 3D computer vision is a rapidly advancing area in computer science. There are many real-world applications that demand high-performance 3D imaging and analysis and, as a result, many new techniques and commercial products have been developed. However, many challenges remain on how to analyse the captured data in a way that is sufficiently fast, robust and accurate for the application. Such challenges include metrology, semantic segmentation, classification and recognition. Thus, 3D imaging, analysis and their applications remain a highly-active research field that will continue to attract intensive attention from the research community with the ultimate goal of fully automating the 3D data capture, analysis and inference pipeline.
Author: Nadia Magnenat Thalmann
Publisher: Springer Nature
ISBN: 3030710025
Category : Computers
Languages : en
Pages : 284
Get Book Here
Book Description
This edited book is one of the first to describe how Autonomous Virtual Humans and Social Robots can interact with real people and be aware of the surrounding world using machine learning and AI. It includes: · Many algorithms related to the awareness of the surrounding world such as the recognition of objects, the interpretation of various sources of data provided by cameras, microphones, and wearable sensors · Deep Learning Methods to provide solutions to Visual Attention, Quality Perception, and Visual Material Recognition · How Face Recognition and Speech Synthesis will replace the traditional mouse and keyboard interfaces · Semantic modeling and rendering and shows how these domains play an important role in Virtual and Augmented Reality Applications. Intelligent Scene Modeling and Human-Computer Interaction explains how to understand the composition and build very complex scenes and emphasizes the semantic methods needed to have an intelligent interaction with them. It offers readers a unique opportunity to comprehend the rapid changes and continuous development in the fields of Intelligent Scene Modeling.
Author: Laura Leal-Taixé
Publisher: Springer
ISBN: 3030110095
Category : Computers
Languages : en
Pages : 771
Get Book Here
Book Description
The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.
Author: Paul L. Rosin
Publisher: Springer
ISBN: 9783030286026
Category : Computers
Languages : en
Pages : 524
Get Book Here
Book Description
This book focuses on the fundamentals and recent advances in RGB-D imaging as well as covering a range of RGB-D applications. The topics covered include: data acquisition, data quality assessment, filling holes, 3D reconstruction, SLAM, multiple depth camera systems, segmentation, object detection, salience detection, pose estimation, geometric modelling, fall detection, autonomous driving, motor rehabilitation therapy, people counting and cognitive service robots. The availability of cheap RGB-D sensors has led to an explosion over the last five years in the capture and application of colour plus depth data. The addition of depth data to regular RGB images vastly increases the range of applications, and has resulted in a demand for robust and real-time processing of RGB-D data. There remain many technical challenges, and RGB-D image processing is an ongoing research area. This book covers the full state of the art, and consists of a series of chapters by internationally renowned experts in the field. Each chapter is written so as to provide a detailed overview of that topic. RGB-D Image Analysis and Processing will enable both students and professional developers alike to quickly get up to speed with contemporary techniques, and apply RGB-D imaging in their own projects.
Author: Guoxiang Zhang
Publisher: Springer Nature
ISBN: 3030961109
Category : Technology & Engineering
Languages : en
Pages : 99
Get Book Here
Book Description
This SpringerBrief presents novel methods of approaching challenging problems in the reconstruction of accurate 3D models and serves as an introduction for further 3D reconstruction methods. It develops a 3D reconstruction system that produces accurate results by cascading multiple novel loop detection, sifting, and optimization methods. The authors offer a fast point cloud registration method that utilizes optimized randomness in random sample consensus for surface loop detection. The text also proposes two methods for surface-loop sifting. One is supported by a sparse-feature-based optimization graph. This graph is more robust to different scan patterns than earlier methods and can cope with tracking failure and recovery. The other is an offline algorithm that can sift loop detections based on their impact on loop optimization results and which is enabled by a dense map posterior metric for 3D reconstruction and mapping performance evaluation works without any costly ground-truth data. The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.
Author: Kyoung-Rok Lee
Publisher:
ISBN: 9781321361797
Category :
Languages : en
Pages : 94
Get Book Here
Book Description
3D reconstruction is the method of creating the shape and appearance of a real scene or objects, given a set of images on the scene. Realistic scene or object reconstruction is essential in many applications such as robotics, computer graphics, Tele- Immersion (TI), and Augmented Reality (AR). This thesis explores accurate, efficient, and robust methods for the 3D reconstruction of static and dynamic objects from RGB-D images. For accurate 3D reconstruction, the depth maps should have high geometric quality and resolution. However, depth maps are often captured at low-quality or low resolution, due to either sensor hardware limitations or errors in estimation. A new sampling-based robust multi-lateral filtering method is proposed herein to improve the resolution and quality of depth data. The enhancement is achieved by selecting reliable depth samples from a neighborhood of pixels and applying multi-lateral filtering using colored images that are both high-quality and high-resolution. Camera pose estimation is one of the most important operations in 3D reconstruction, since any minor error in this process may distort the resulting reconstruction. We present a robust method for camera tracking and surface mapping using a handheld RGB-D camera, which is effective for challenging situations such as during fast camera motion or in geometrically featureless scenes. This is based on the quaternion-based orientation estimation method for initial sparse estimation and a weighted Iterative Closest Point (ICP) method for dense estimation to achieve a better rate of convergence for both the optimization and accuracy of the resulting trajectory. We present a novel approach for the reconstruction of static object/scene with realistic surface geometry using a handheld RGB-D camera. To obtain high-resolution RGB images, an additional HD camera is attached to the top of a Kinect and is calibrated to reconstruct a 3D model with realistic surface geometry and high-quality color textures. We extend our depth map refinement method by utilizing high frequency information in color images to recover finer-scale surface geometry. In addition, we use our robust camera pose estimation to estimate the orientation of the camera in the global coordinate system accurately. For the reconstruction of moving objects, a novel dynamic scene reconstruction system using multiple commodity depth cameras is proposed. Instead of using expensive multi-view scene capturing setups, our system only requires four Kinects, which are carefully located to generate full 3D surface models of objects. We introduce a novel depth synthesis method for point cloud densification and noise removal in the depth data. In addition, a new weighting function is presented to overcome the drawbacks of the existing volumetric representation method.
