3D model-based human motion capture

Master'sMASTER OF ENGINEERIN

Practical Euclidean reconstruction of buildings.

Chou Yun-Sum, Bailey.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 89-92).Abstracts in English and Chinese.List of SymbolChapter Chapter 1 --- IntroductionChapter 1.1 --- The Goal: Euclidean Reconstruction --- p.1Chapter 1.2 --- Historical background --- p.2Chapter 1.3 --- Scope of the thesis --- p.2Chapter 1.4 --- Thesis Outline --- p.3Chapter Chapter 2 --- An introduction to stereo vision and 3D shape reconstructionChapter 2.1 --- Homogeneous Coordinates --- p.4Chapter 2.2 --- Camera ModelChapter 2.2.1 --- Pinhole Camera Model --- p.5Chapter 2.3 --- Camera Calibration --- p.11Chapter 2.4 --- Geometry of Binocular System --- p.14Chapter 2.5 --- Stereo Matching --- p.15Chapter 2.5.1 --- Accuracy of Corresponding Point --- p.17Chapter 2.5.2 --- The Stereo Matching Approach --- p.18Chapter 2.5.2.1 --- Intensity-based stereo matching --- p.19Chapter 2.5.2.2 --- Feature-based stereo matching --- p.20Chapter 2.5.3 --- Matching Constraints --- p.20Chapter 2.6 --- 3D Reconstruction --- p.22Chapter 2.7 --- Recent development on self calibration --- p.24Chapter 2.8 --- Summary of the Chapter --- p.25Chapter Chapter 3 --- Camera CalibrationChapter 3.1 --- Introduction --- p.26Chapter 3.2 --- Camera Self-calibration --- p.27Chapter 3.3 --- Self-calibration under general camera motion --- p.27Chapter 3.3.1 --- The absolute Conic Based Techniques --- p.28Chapter 3.3.2 --- A Stratified approach for self-calibration by Pollefeys --- p.33Chapter 3.3.3 --- Pollefeys self-calibration with Absolute Quadric --- p.34Chapter 3.3.4 --- Newsam's self-calibration with linear algorithm --- p.34Chapter 3.4 --- Camera Self-calibration under specially designed motion sequenceChapter 3.4. 1 --- Hartley's self-calibration by pure rotations --- p.35Chapter 3.4.1.1 --- Summary of the AlgorithmChapter 3.4.2 --- Pollefeys self-calibration with variant focal length --- p.36Chapter 3.4.2.1 --- Summary of the AlgorithmChapter 3.4.3 --- Faugeras self-calibration of a 1D Projective Camera --- p.38Chapter 3.5 --- Summary of the Chapter --- p.39Chapter Chapter 4 --- Self-calibration under Planar motionsChapter 4.1 --- Introduction --- p.40Chapter 4.2 --- 1D Projective Camera Self-calibration --- p.41Chapter 4.2.1 --- 1-D camera model --- p.42Chapter 4.2.2 --- 1-D Projective Camera Self-calibration Algorithms --- p.44Chapter 4.2.3 --- Planar motion detection --- p.45Chapter 4.2.4 --- Self-calibration under horizontal planar motions --- p.46Chapter 4.2.5 --- Self-calibration under three different planar motions --- p.47Chapter 4.2.6 --- Result analysis on self-calibration Experiments --- p.49Chapter 4.3 --- Essential Matrix and Triangulation --- p.51Chapter 4.4 --- Merge of Partial 3D models --- p.51Chapter 4.5 --- Summary of the Reconstruction Algorithms --- p.53Chapter 4.6 --- Experimental ResultsChapter 4.6.1 --- Experiment 1 : A Simulated Box --- p.54Chapter 4.6.2 --- Experiment 2 : A Real Building --- p.57Chapter 4.6.3 --- Experiment 3 : A Sun Flower --- p.58Chapter 4.7 --- Conclusion --- p.59Chapter Chapter 5 --- Building Reconstruction using a linear camera self- calibration techniqueChapter 5.1 --- Introduction --- p.60Chapter 5.2 --- Metric Reconstruction from Partially Calibrated imageChapter 5.2.1 --- Partially Calibrated Camera --- p.62Chapter 5.2.2 --- Optimal Computation of Fundamental Matrix (F) --- p.63Chapter 5.2.3 --- Linearly Recovering Two Focal Lengths from F --- p.64Chapter 5.2.4 --- Essential Matrix and Triangulation --- p.66Chapter 5.3 --- Experiments and Discussions --- p.67Chapter 5.4 --- Conclusion --- p.71Chapter Chapter 6 --- Refine the basic model with detail depth information by a Model-Based Stereo techniqueChapter 6.1 --- Introduction --- p.72Chapter 6.2 --- Model Based Epipolar GeometryChapter 6.2.1 --- Overview --- p.74Chapter 6.2.2 --- Warped offset image preparation --- p.76Chapter 6.2.3 --- Epipolar line calculation --- p.78Chapter 6.2.4 --- Actual corresponding point finding by stereo matching --- p.80Chapter 6.2.5 --- Actual 3D point generated by Triangulation --- p.80Chapter 6.3 --- Summary of the Algorithms --- p.81Chapter 6.4 --- Experiments and discussions --- p.83Chapter 6.5 --- Conclusion --- p.85Chapter Chapter 7 --- ConclusionsChapter 7.1 --- Summary --- p.86Chapter 7.2 --- Future Work --- p.88BIBLIOGRAPHY --- p.8

THREE-DIMENSIONAL VISION FOR STRUCTURE AND MOTION ESTIMATION

1997/1998Questa tesi, intitolata Visione Tridimensionale per la stima di Struttura e Moto, tratta di tecniche di Visione Artificiale per la stima delle proprietà geometriche del mondo tridimensionale a partire da immagini numeriche. Queste proprietà sono essenziali per il riconoscimento e la classificazione di oggetti, la navigazione di veicoli mobili autonomi, il reverse engineering e la sintesi di ambienti virtuali. In particolare, saranno descritti i moduli coinvolti nel calcolo della struttura della scena a partire dalle immagini, e verranno presentati contributi originali nei seguenti campi. Rettificazione di immagini steroscopiche. Viene presentato un nuovo algoritmo per la rettificazione, il quale trasforma una coppia di immagini stereoscopiche in maniera che punti corrispondenti giacciano su linee orizzontali con lo stesso indice. Prove sperimentali dimostrano il corretto comportamento del metodo, come pure la trascurabile perdita di accuratezza nella ricostruzione tridimensionale quando questa sia ottenuta direttamente dalle immagini rettificate. Calcolo delle corrispondenze in immagini stereoscopiche. Viene analizzato il problema della stereovisione e viene presentato un un nuovo ed efficiente algoritmo per l'identificazione di coppie di punti corrispondenti, capace di calcolare in modo robusto la disparità stereoscopica anche in presenza di occlusioni. L'algoritmo, chiamato SMW, usa uno schema multi-finestra adattativo assieme al controllo di coerenza destra-sinistra per calcolare la disparità e l'incertezza associata. Gli esperimenti condotti con immagini sintetiche e reali mostrano che SMW sortisce un miglioramento in accuratezza ed efficienza rispetto a metodi simili Inseguimento di punti salienti. L'inseguitore di punti salienti di Shi-Tomasi- Kanade viene migliorato introducendo uno schema automatico per lo scarto di punti spuri basato sulla diagnostica robusta dei campioni periferici ( outliers ). Gli esperimenti con immagini sintetiche e reali confermano il miglioramento rispetto al metodo originale, sia qualitativamente che quantitativamente. Ricostruzione non calibrata. Viene presentata una rassegna ragionata dei metodi per la ricostruzione di un modello tridimensionale della scena, a partire da una telecamera che si muove liberamente e di cui non sono noti i parametri interni. Il contributo consiste nel fornire una visione critica e unificata delle più recenti tecniche. Una tale rassegna non esiste ancora in letterarura. Moto tridimensionale. Viene proposto un algoritmo robusto per registrate e calcolare le corrispondenze in due insiemi di punti tridimensionali nei quali vi sia un numero significativo di elementi mancanti. Il metodo, chiamato RICP, sfrutta la stima robusta con la Minima Mediana dei Quadrati per eliminare l'effetto dei campioni periferici. Il confronto sperimentale con una tecnica simile, ICP, mostra la superiore robustezza e affidabilità di RICP.This thesis addresses computer vision techniques estimating geometrie properties of the 3-D world /rom digital images. Such properties are essential for object recognition and classification, mobile robots navigation, reverse engineering and synthesis of virtual environments. In particular, this thesis describes the modules involved in the computation of the structure of a scene given some images, and offers original contributions in the following fields. Stereo pairs rectification. A novel rectification algorithm is presented, which transform a stereo pair in such a way that corresponding points in the two images lie on horizontal lines with the same index. Experimental tests prove the correct behavior of the method, as well as the negligible decrease oLthe accuracy of 3-D reconstruction if performed from the rectified images directly. Stereo matching. The problem of computational stereopsis is analyzed, and a new, efficient stereo matching algorithm addressing robust disparity estimation in the presence of occlusions is presented. The algorithm, called SMW, is an adaptive, multi-window scheme using left-right consistency to compute disparity and its associated uncertainty. Experiments with both synthetic and real stereo pairs show how SMW improves on closely related techniques for both accuracy and efficiency. Features tracking. The Shi-Tomasi-Kanade feature tracker is improved by introducing an automatic scheme for rejecting spurious features, based on robust outlier diagnostics. Experiments with real and synthetic images confirm the improvement over the original tracker, both qualitatively and quantitatively. 111 Uncalibrated vision. A review on techniques for computing a three-dimensional model of a scene from a single moving camera, with unconstrained motion and unknown parameters is presented. The contribution is to give a critical, unified view of some of the most promising techniques. Such review does not yet exist in the literature. 3-D motion. A robust algorithm for registering and finding correspondences in two sets of 3-D points with significant percentages of missing data is proposed. The method, called RICP, exploits LMedS robust estimation to withstand the effect of outliers. Experimental comparison with a closely related technique, ICP, shows RICP's superior robustness and reliability.XI Ciclo1968Versione digitalizzata della tesi di dottorato cartacea

Variable Resolution & Dimensional Mapping For 3d Model Optimization

Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data

The Extraction and Use of Image Planes for Three-dimensional Metric Reconstruction

The three-dimensional (3D) metric reconstruction of a scene from two-dimensional images is a fundamental problem in Computer Vision. The major bottleneck in the process of retrieving such structure lies in the task of recovering the camera parameters. These parameters can be calculated either through a pattern-based calibration procedure, which requires an accurate knowledge of the scene, or using a more flexible approach, known as camera autocalibration, which exploits point correspondences across images. While pattern-based calibration requires the presence of a calibration object, autocalibration constraints are often cast into nonlinear optimization problems which are often sensitive to both image noise and initialization. In addition, autocalibration fails for some particular motions of the camera. To overcome these problems, we propose to combine scene and autocalibration constraints and address in this thesis (a) the problem of extracting geometric information of the scene from uncalibrated images, (b) the problem of obtaining a robust estimate of the affine calibration of the camera, and (c) the problem of upgrading and refining the affine calibration into a metric one. In particular, we propose a method for identifying the major planar structures in a scene from images and another method to recognize parallel pairs of planes whenever these are available. The identified parallel planes are then used to obtain a robust estimate of both the affine and metric 3D structure of the scene without resorting to the traditional error prone calculation of vanishing points. We also propose a refinement method which, unlike existing ones, is capable of simultaneously incorporating plane parallelism and perpendicularity constraints in the autocalibration process. Our experiments demonstrate that the proposed methods are robust to image noise and provide satisfactory results