3D Scene Geometry Estimation from 360∘^\circ Imagery: A Survey

This paper provides a comprehensive survey on pioneer and state-of-the-art 3D scene geometry estimation methodologies based on single, two, or multiple images captured under the omnidirectional optics. We first revisit the basic concepts of the spherical camera model, and review the most common acquisition technologies and representation formats suitable for omnidirectional (also called 360$^\circ$, spherical or panoramic) images and videos. We then survey monocular layout and depth inference approaches, highlighting the recent advances in learning-based solutions suited for spherical data. The classical stereo matching is then revised on the spherical domain, where methodologies for detecting and describing sparse and dense features become crucial. The stereo matching concepts are then extrapolated for multiple view camera setups, categorizing them among light fields, multi-view stereo, and structure from motion (or visual simultaneous localization and mapping). We also compile and discuss commonly adopted datasets and figures of merit indicated for each purpose and list recent results for completeness. We conclude this paper by pointing out current and future trends.Comment: Published in ACM Computing Survey

360º Indoors image processing for 3D model reconstruction

In this modern age of computer technology we are pushing the unimaginable limits of our reality. One of the human desires with these advances is to digitise the vast amount of information that is present in our reality. An important source of information is the 3-dimensional space in which we live. Especially indoors environments that we frequently occupy, for example, living places. With the proliferation of photographing devices, the development of cheap omnidirectional cameras has been one of the interests. So nowadays it is quite easy to obtain spatial data of interior spaces in form of equirectangular images. In this project we study the problem of 3D Indoors Model Reconstruction from Spherical Images. Though, we study it under perspective based methods as it is possible to perform the conversion from one to other. We first formally specify the problem to be solved. We find many different specifications and describe reconstruction methods for some of them. We choose one specification for our use case. Most of the methods require feature extraction and matching, and then performing multi-view geometry estimation. We continue the study of these methods in the experimentation phase. We propose different hypothesis relevant to different steps, perform experiments and form our conclusions. We finish our work by implementing a very simple system solving this problem, making use of ASIFT feature extractor, FLANN kD-Tree feature matcher, and OpenCV's essential matrix estimation algorithm.En aquesta era moderna de la tecnologia de computadors estem empenyent els líımits inimaginables de la nostra realitat. Una de les aspiracions humanes amb aquests avenços és la digitalització de l’enorme quantitat d’informació present en la nostra realitat. Una de les fonts importants d’informació és l’espai 3-dimensional en el que vivim. Especialment, els entorns interiors que habitem, per exemple, els habitatges. Amb la proliferació dels dispositius fotogràfics, el desenvolupament de càmeres omnidireccionals barates ha estat un dels interessos. Per aquest motiu, avui en dia és molt fàcil obtenir dades espacials dels espais interiors en forma d’imatges equirectangulars. En aquest projecte estudiem el problema de la Reconstrucció de Models 3D d’Interiors a partir d’Imatges Esfèriques. Tanmateix, estudiem el problema fent ús de mètodes basats en la perspectiva ja que és possible fer la conversió d’un a l’altre. En primer lloc, especifiquem formalment el problema a resoldre. A continuació, trobem diverses especificacions i descrivim mètodes de reconstruccions per algunes d’elles. Seleccionem una especificaciò pel nostre cas d’ús. La majoria de mètodes utilitzen feature extraction, feature matching i epipolar geometry. Continuem l’estudi amb la fase d’experimentació. Proposem hipòtesis rellevants a diferents passos, realitzem els experiments i extraiem conclusions. Acabem el treball implementant un sistema simple resolent el problema, fent ús de ASIFT feature extractor, FLANN kD- Tree feature matcher, i l’algorisme d’OpenCV per l’aproximació de la matriu essencialEn esta era moderna de tecnología de computadores estamos empujando los límites inimaginables de nuestra realidad. Una de las aspiraciones humanas con estos avances es la digitalización de la tremenda cantidad de información presente en nuestra realidad. Una de las importantes fuentes de información es el espacio 3-dimensional en que vivimos. Especialmente los entornos interiores que habitamos, por ejemplo, las viviendas. Con la proliferación de los dispositivos fotográficos, el desarrollo de cámaras omnidireccionales baratas ha sido uno de los intereses. Por ello, hoy en día es muy fácil de obtener datos espaciales de los espacios interiores en forma de imágenes equirectangulares. En este proyecto estudiamos el problema de Reconstrucción de Modelos 3D de Interiores desde Imágenes Esféricas. Sin embargo, estudiamos el problema bajo métodos basados en la perspectiva ya que es posible hacer la conversión de uno al otro. Primero especificamos formalmente el problema a resolver. Encontramos distintas especificaciones y describimos métodos de reconstruccion para algunas de ellas. Seleccionamos una especificación para nuestro caso de uso. La mayoría de métodos utilizan feature extraction, feature matching, y epipolar geometry. Continuamos el estudio en la fase de experimentación. Proponemos hipótesis relevantes a diferentes pasos, realizamos los experimentos y sacamos conclusiones. Acabamos el trabajo implementando un sistema simple resolviendo el problema, haciendo uso de ASIFT feature extractor, FLANN kD-Tree feature matcher, y el algoritmo de OpenCV para la aproximación de la matriz esencial

Interactive illumination and navigation control in an image-based environment.

Fu Chi-wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 141-149).Abstract --- p.iAcknowledgments --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction to Image-based Rendering --- p.1Chapter 1.2 --- Scene Complexity Independent Property --- p.2Chapter 1.3 --- Application of this Research Work --- p.3Chapter 1.4 --- Organization of this Thesis --- p.4Chapter 2 --- Illumination Control --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Apparent BRDF of Pixel --- p.8Chapter 2.3 --- Sampling Illumination Information --- p.11Chapter 2.4 --- Re-rendering --- p.13Chapter 2.4.1 --- Light Direction --- p.15Chapter 2.4.2 --- Light Intensity --- p.15Chapter 2.4.3 --- Multiple Light Sources --- p.15Chapter 2.4.4 --- Type of Light Sources --- p.18Chapter 2.5 --- Data Compression --- p.22Chapter 2.5.1 --- Intra-pixel coherence --- p.22Chapter 2.5.2 --- Inter-pixel coherence --- p.22Chapter 2.6 --- Implementation and Result --- p.22Chapter 2.6.1 --- An Interactive Viewer --- p.22Chapter 2.6.2 --- Lazy Re-rendering --- p.24Chapter 2.7 --- Conclusion --- p.24Chapter 3 --- Navigation Control - Triangle-based Warping Rule --- p.29Chapter 3.1 --- Introduction to Navigation Control --- p.29Chapter 3.2 --- Related Works --- p.30Chapter 3.3 --- Epipolar Geometry (Perspective Projection Manifold) --- p.31Chapter 3.4 --- Drawing Order for Pixel-Sized Entities --- p.35Chapter 3.5 --- Triangle-based Image Warping --- p.36Chapter 3.5.1 --- Image-based Triangulation --- p.36Chapter 3.5.2 --- Image-based Visibility Sorting --- p.40Chapter 3.5.3 --- Topological Sorting --- p.44Chapter 3.6 --- Results --- p.46Chapter 3.7 --- Conclusion --- p.48Chapter 4 --- Panoramic Projection Manifold --- p.52Chapter 4.1 --- Epipolar Geometry (Spherical Projection Manifold) --- p.53Chapter 4.2 --- Image Triangulation --- p.56Chapter 4.2.1 --- Optical Flow --- p.56Chapter 4.2.2 --- Image Gradient and Potential Function --- p.57Chapter 4.2.3 --- Triangulation --- p.58Chapter 4.3 --- Image-based Visibility Sorting --- p.58Chapter 4.3.1 --- Mapping Criteria --- p.58Chapter 4.3.2 --- Ordering of Two Triangles --- p.59Chapter 4.3.3 --- Graph Construction and Topological Sort --- p.63Chapter 4.4 --- Results --- p.63Chapter 4.5 --- Conclusion --- p.65Chapter 5 --- Panoramic-based Navigation using Real Photos --- p.69Chapter 5.1 --- Introduction --- p.69Chapter 5.2 --- System Overview --- p.71Chapter 5.3 --- Correspondence Matching --- p.72Chapter 5.3.1 --- Basic Model of Epipolar Geometry --- p.72Chapter 5.3.2 --- Epipolar Geometry between two Neighbor Panoramic Nodes --- p.73Chapter 5.3.3 --- Line and Patch Correspondence Matching --- p.74Chapter 5.4 --- Triangle-based Warping --- p.75Chapter 5.4.1 --- Why Warp Triangle --- p.75Chapter 5.4.2 --- Patch and Layer Construction --- p.76Chapter 5.4.3 --- Triangulation and Mesh Subdivision --- p.76Chapter 5.4.4 --- Layered Triangle-based Warping --- p.77Chapter 5.5 --- Implementation --- p.78Chapter 5.5.1 --- Image Sampler and Panoramic Stitcher --- p.78Chapter 5.5.2 --- Interactive Correspondence Matcher and Triangulation --- p.79Chapter 5.5.3 --- Basic Panoramic Viewer --- p.79Chapter 5.5.4 --- Formulating Drag Vector and vn --- p.80Chapter 5.5.5 --- Controlling Walkthrough Parameter --- p.82Chapter 5.5.6 --- Interactive Web-based Panoramic Viewer --- p.83Chapter 5.6 --- Results --- p.84Chapter 5.7 --- Conclusion and Possible Enhancements --- p.84Chapter 6 --- Compositing Warped Images for Object-based Viewing --- p.89Chapter 6.1 --- Modeling Object-based Viewing --- p.89Chapter 6.2 --- Triangulation and Convex Hull Criteria --- p.92Chapter 6.3 --- Warping Multiple Views --- p.94Chapter 6.3.1 --- Method I --- p.95Chapter 6.3.2 --- Method II --- p.95Chapter 6.3.3 --- Method III --- p.95Chapter 6.4 --- Results --- p.97Chapter 6.5 --- Conclusion --- p.100Chapter 7 --- Complete Rendering Pipeline --- p.107Chapter 7.1 --- Reviews on Illumination and Navigation --- p.107Chapter 7.1.1 --- Illumination Rendering Pipeline --- p.107Chapter 7.1.2 --- Navigation Rendering Pipeline --- p.108Chapter 7.2 --- Analysis of the Two Rendering Pipelines --- p.109Chapter 7.2.1 --- Combination on the Architectural Level --- p.109Chapter 7.2.2 --- Ensuring Physical Correctness --- p.111Chapter 7.3 --- Generalizing Apparent BRDF --- p.112Chapter 7.3.1 --- Difficulties to Encode BRDF with Spherical Harmonics --- p.112Chapter 7.3.2 --- Generalize Apparent BRDF --- p.112Chapter 7.3.3 --- Related works for Encoding the generalized apparent BRDF --- p.113Chapter 7.4 --- Conclusion --- p.116Chapter 8 --- Conclusion --- p.117Chapter A --- Spherical Harmonics --- p.120Chapter B --- It is Rare for Cycles to Exist in the Drawing Order Graph --- p.123Chapter B.1 --- Theorem 3 --- p.123Chapter B.2 --- Inside and Outside-directed Triangles in a Triangular Cycle --- p.125Chapter B.2.1 --- Assumption --- p.126Chapter B.2.2 --- Inside-directed and Outside-directed triangles --- p.126Chapter B.3 --- Four Possible Cases to Form a Cycle --- p.127Chapter B.3.1 --- Case(l) Triangular Fan --- p.128Chapter B.3.2 --- Case(2) Two Outside-directed Triangles --- p.129Chapter B.3.3 --- Case(3) Three Outside-directed Triangles --- p.130Chapter B.3.4 --- Case(4) More than Three Outside-directed Triangles --- p.131Chapter B.4 --- Experiment --- p.132Chapter C --- Deriving the Epipolar Line Formula on Cylindrical Projection Manifold --- p.133Chapter C.1 --- Notations --- p.133Chapter C.2 --- General Formula --- p.134Chapter C.3 --- Simplify the General Formula to a Sine Curve --- p.137Chapter C.4 --- Show that the Epipolar Line is a Sine Curve Segment --- p.139Chapter D --- Publications Related to this Research Work --- p.141Bibliography --- p.14

Camera positioning for 3D panoramic image rendering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Virtual camera realisation and the proposition of trapezoidal camera architecture are the two broad contributions of this thesis. Firstly, multiple camera and their arrangement constitute a critical component which affect the integrity of visual content acquisition for multi-view video. Currently, linear, convergence, and divergence arrays are the prominent camera topologies adopted. However, the large number of cameras required and their synchronisation are two of prominent challenges usually encountered. The use of virtual cameras can significantly reduce the number of physical cameras used with respect to any of the known camera structures, hence adequately reducing some of the other implementation issues. This thesis explores to use image-based rendering with and without geometry in the implementations leading to the realisation of virtual cameras. The virtual camera implementation was carried out from the perspective of depth map (geometry) and use of multiple image samples (no geometry). Prior to the virtual camera realisation, the generation of depth map was investigated using region match measures widely known for solving image point correspondence problem. The constructed depth maps have been compare with the ones generated using the dynamic programming approach. In both the geometry and no geometry approaches, the virtual cameras lead to the rendering of views from a textured depth map, construction of 3D panoramic image of a scene by stitching multiple image samples and performing superposition on them, and computation of virtual scene from a stereo pair of panoramic images. The quality of these rendered images were assessed through the use of either objective or subjective analysis in Imatest software. Further more, metric reconstruction of a scene was performed by re-projection of the pixel points from multiple image samples with a single centre of projection. This was done using sparse bundle adjustment algorithm. The statistical summary obtained after the application of this algorithm provides a gauge for the efficiency of the optimisation step. The optimised data was then visualised in Meshlab software environment, hence providing the reconstructed scene. Secondly, with any of the well-established camera arrangements, all cameras are usually constrained to the same horizontal plane. Therefore, occlusion becomes an extremely challenging problem, and a robust camera set-up is required in order to resolve strongly the hidden part of any scene objects. To adequately meet the visibility condition for scene objects and given that occlusion of the same scene objects can occur, a multi-plane camera structure is highly desirable. Therefore, this thesis also explore trapezoidal camera structure for image acquisition. The approach here is to assess the feasibility and potential of several physical cameras of the same model being sparsely arranged on the edge of an efficient trapezoid graph. This is implemented both Matlab and Maya. The quality of the depth maps rendered in Matlab are better in Quality