Chebyshev and Conjugate Gradient Filters for Graph Image Denoising

In 3D image/video acquisition, different views are often captured with varying noise levels across the views. In this paper, we propose a graph-based image enhancement technique that uses a higher quality view to enhance a degraded view. A depth map is utilized as auxiliary information to match the perspectives of the two views. Our method performs graph-based filtering of the noisy image by directly computing a projection of the image to be filtered onto a lower dimensional Krylov subspace of the graph Laplacian. We discuss two graph spectral denoising methods: first using Chebyshev polynomials, and second using iterations of the conjugate gradient algorithm. Our framework generalizes previously known polynomial graph filters, and we demonstrate through numerical simulations that our proposed technique produces subjectively cleaner images with about 1-3 dB improvement in PSNR over existing polynomial graph filters.Comment: 6 pages, 6 figures, accepted to 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW

Livrable D2.2 of the PERSEE project : Analyse/Synthese de Texture

Livrable D2.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D2.2 du projet. Son titre : Analyse/Synthese de Textur

Proxy-guided Image-based Rendering for Mobile Devices

VR headsets and hand-held devices are not powerful enough to render complex scenes in real-time. A server can take on the rendering task, but network latency prohibits a good user experience. We present a new image-based rendering (IBR) architecture for masking the latency. It runs in real-time even on very weak mobile devices, supports modern game engine graphics, and maintains high visual quality even for large view displacements. We propose a novel server-side dual-view representation that leverages an optimally-placed extra view and depth peeling to provide the client with coverage for filling disocclusion holes. This representation is directly rendered in a novel wide-angle projection with favorable directional parameterization. A new client-side IBR algorithm uses a pre-transmitted level-of-detail proxy with an encaging simplification and depth-carving to maintain highly complex geometric detail. We demonstrate our approach with typical VR / mobile gaming applications running on mobile hardware. Our technique compares favorably to competing approaches according to perceptual and numerical comparisons

Motion parallax for 360° RGBD video

We present a method for adding parallax and real-time playback of 360° videos in Virtual Reality headsets. In current video players, the playback does not respond to translational head movement, which reduces the feeling of immersion, and causes motion sickness for some viewers. Given a 360° video and its corresponding depth (provided by current stereo 360° stitching algorithms), a naive image-based rendering approach would use the depth to generate a 3D mesh around the viewer, then translate it appropriately as the viewer moves their head. However, this approach breaks at depth discontinuities, showing visible distortions, whereas cutting the mesh at such discontinuities leads to ragged silhouettes and holes at disocclusions. We address these issues by improving the given initial depth map to yield cleaner, more natural silhouettes. We rely on a three-layer scene representation, made up of a foreground layer and two static background layers, to handle disocclusions by propagating information from multiple frames for the first background layer, and then inpainting for the second one. Our system works with input from many of today''s most popular 360° stereo capture devices (e.g., Yi Halo or GoPro Odyssey), and works well even if the original video does not provide depth information. Our user studies confirm that our method provides a more compelling viewing experience than without parallax, increasing immersion while reducing discomfort and nausea

Efficient and High-Quality Rendering of Higher-Order Geometric Data Representations

Computer-Aided Design (CAD) bezeichnet den Entwurf industrieller Produkte mit Hilfe von virtuellen 3D Modellen. Ein CAD-Modell besteht aus parametrischen Kurven und Flächen, in den meisten Fällen non-uniform rational B-Splines (NURBS). Diese mathematische Beschreibung wird ebenfalls zur Analyse, Optimierung und Präsentation des Modells verwendet. In jeder dieser Entwicklungsphasen wird eine unterschiedliche visuelle Darstellung benötigt, um den entsprechenden Nutzern ein geeignetes Feedback zu geben. Designer bevorzugen beispielsweise illustrative oder realistische Darstellungen, Ingenieure benötigen eine verständliche Visualisierung der Simulationsergebnisse, während eine immersive 3D Darstellung bei einer Benutzbarkeitsanalyse oder der Designauswahl hilfreich sein kann. Die interaktive Darstellung von NURBS-Modellen und -Simulationsdaten ist jedoch aufgrund des hohen Rechenaufwandes und der eingeschränkten Hardwareunterstützung eine große Herausforderung. Diese Arbeit stellt vier neuartige Verfahren vor, welche sich mit der interaktiven Darstellung von NURBS-Modellen und Simulationensdaten befassen. Die vorgestellten Algorithmen nutzen neue Fähigkeiten aktueller Grafikkarten aus, um den Stand der Technik bezüglich Qualität, Effizienz und Darstellungsgeschwindigkeit zu verbessern. Zwei dieser Verfahren befassen sich mit der direkten Darstellung der parametrischen Beschreibung ohne Approximationen oder zeitaufwändige Vorberechnungen. Die dabei vorgestellten Datenstrukturen und Algorithmen ermöglichen die effiziente Unterteilung, Klassifizierung, Tessellierung und Darstellung getrimmter NURBS-Flächen und einen interaktiven Ray-Casting-Algorithmus für die Isoflächenvisualisierung von NURBSbasierten isogeometrischen Analysen. Die weiteren zwei Verfahren beschreiben zum einen das vielseitige Konzept der programmierbaren Transparenz für illustrative und verständliche Visualisierungen tiefenkomplexer CAD-Modelle und zum anderen eine neue hybride Methode zur Reprojektion halbtransparenter und undurchsichtiger Bildinformation für die Beschleunigung der Erzeugung von stereoskopischen Bildpaaren. Die beiden letztgenannten Ansätze basieren auf rasterisierter Geometrie und sind somit ebenfalls für normale Dreiecksmodelle anwendbar, wodurch die Arbeiten auch einen wichtigen Beitrag in den Bereichen der Computergrafik und der virtuellen Realität darstellen. Die Auswertung der Arbeit wurde mit großen, realen NURBS-Datensätzen durchgeführt. Die Resultate zeigen, dass die direkte Darstellung auf Grundlage der parametrischen Beschreibung mit interaktiven Bildwiederholraten und in subpixelgenauer Qualität möglich ist. Die Einführung programmierbarer Transparenz ermöglicht zudem die Umsetzung kollaborativer 3D Interaktionstechniken für die Exploration der Modelle in virtuellenUmgebungen sowie illustrative und verständliche Visualisierungen tiefenkomplexer CAD-Modelle. Die Erzeugung stereoskopischer Bildpaare für die interaktive Visualisierung auf 3D Displays konnte beschleunigt werden. Diese messbare Verbesserung wurde zudem im Rahmen einer Nutzerstudie als wahrnehmbar und vorteilhaft befunden.In computer-aided design (CAD), industrial products are designed using a virtual 3D model. A CAD model typically consists of curves and surfaces in a parametric representation, in most cases, non-uniform rational B-splines (NURBS). The same representation is also used for the analysis, optimization and presentation of the model. In each phase of this process, different visualizations are required to provide an appropriate user feedback. Designers work with illustrative and realistic renderings, engineers need a comprehensible visualization of the simulation results, and usability studies or product presentations benefit from using a 3D display. However, the interactive visualization of NURBS models and corresponding physical simulations is a challenging task because of the computational complexity and the limited graphics hardware support. This thesis proposes four novel rendering approaches that improve the interactive visualization of CAD models and their analysis. The presented algorithms exploit latest graphics hardware capabilities to advance the state-of-the-art in terms of quality, efficiency and performance. In particular, two approaches describe the direct rendering of the parametric representation without precomputed approximations and timeconsuming pre-processing steps. New data structures and algorithms are presented for the efficient partition, classification, tessellation, and rendering of trimmed NURBS surfaces as well as the first direct isosurface ray-casting approach for NURBS-based isogeometric analysis. The other two approaches introduce the versatile concept of programmable order-independent semi-transparency for the illustrative and comprehensible visualization of depth-complex CAD models, and a novel method for the hybrid reprojection of opaque and semi-transparent image information to accelerate stereoscopic rendering. Both approaches are also applicable to standard polygonal geometry which contributes to the computer graphics and virtual reality research communities. The evaluation is based on real-world NURBS-based models and simulation data. The results show that rendering can be performed directly on the underlying parametric representation with interactive frame rates and subpixel-precise image results. The computational costs of additional visualization effects, such as semi-transparency and stereoscopic rendering, are reduced to maintain interactive frame rates. The benefit of this performance gain was confirmed by quantitative measurements and a pilot user study

3D modeling of indoor environments for a robotic security guard

Autonomous mobile robots will play a major role in future security and surveillance tasks for large scale environments such as shopping malls, airports, hospitals and museums. Robotic security guards will autonomously survey such environments, unless a remote human operator takes over control. In this context a 3D model can convey much more useful information than the typical 2D maps used in many robotic applications today, both for visualisation of information and as human machine interface for remote control. This paper addresses the challenge of building such a model of a large environment (50m x 60m) using data from the robot’s own sensors: a 2D laser scanner and a panoramic camera. The data are processed in a pipeline that comprises automatic, semi-automatic and manual stages. The user can interact with the reconstruction process where necessary to ensure robustness and completeness of the model. A hybrid representation, tailored to the application, has been chosen: floors and walls are represented efficiently by textured planes. Non-planar structures like stairs and tables, which are represented by point clouds, can be added if desired. Our methods to extract these structures include: simultaneous localization and mapping in 2D and wall extraction based on laser scanner range data, building textures from multiple omni-directional images using multi-resolution blending, and calculation of 3D geometry by a graph cut stereo technique. Various renderings illustrate the usability of the model for visualising the security guard’s position and environment

Real-time rendering of large surface-scanned range data natively on a GPU

This thesis presents research carried out for the visualisation of surface anatomy data stored as large range images such as those produced by stereo-photogrammetric, and other triangulation-based capture devices. As part of this research, I explored the use of points as a rendering primitive as opposed to polygons, and the use of range images as the native data representation. Using points as a display primitive as opposed to polygons required the creation of a pipeline that solved problems associated with point-based rendering. The problems inves tigated were scattered-data interpolation (a common problem with point-based rendering), multi-view rendering, multi-resolution representations, anti-aliasing, and hidden-point re- moval. In addition, an efficient real-time implementation on the GPU was carried out

Image Based View Synthesis

This dissertation deals with the image-based approach to synthesize a virtual scene using sparse images or a video sequence without the use of 3D models. In our scenario, a real dynamic or static scene is captured by a set of un-calibrated images from different viewpoints. After automatically recovering the geometric transformations between these images, a series of photo-realistic virtual views can be rendered and a virtual environment covered by these several static cameras can be synthesized. This image-based approach has applications in object recognition, object transfer, video synthesis and video compression. In this dissertation, I have contributed to several sub-problems related to image based view synthesis. Before image-based view synthesis can be performed, images need to be segmented into individual objects. Assuming that a scene can approximately be described by multiple planar regions, I have developed a robust and novel approach to automatically extract a set of affine or projective transformations induced by these regions, correctly detect the occlusion pixels over multiple consecutive frames, and accurately segment the scene into several motion layers. First, a number of seed regions using correspondences in two frames are determined, and the seed regions are expanded and outliers are rejected employing the graph cuts method integrated with level set representation. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, the occlusion order constraints on multiple frames are explored, which guarantee that the occlusion area increases with the temporal order in a short period and effectively maintains segmentation consistency over multiple consecutive frames. Then the correct layer segmentation is obtained by using a graph cuts algorithm, and the occlusions between the overlapping layers are explicitly determined. Several experimental results are demonstrated to show that our approach is effective and robust. Recovering the geometrical transformations among images of a scene is a prerequisite step for image-based view synthesis. I have developed a wide baseline matching algorithm to identify the correspondences between two un-calibrated images, and to further determine the geometric relationship between images, such as epipolar geometry or projective transformation. In our approach, a set of salient features, edge-corners, are detected to provide robust and consistent matching primitives. Then, based on the Singular Value Decomposition (SVD) of an affine matrix, we effectively quantize the search space into two independent subspaces for rotation angle and scaling factor, and then we use a two-stage affine matching algorithm to obtain robust matches between these two frames. The experimental results on a number of wide baseline images strongly demonstrate that our matching method outperforms the state-of-art algorithms even under the significant camera motion, illumination variation, occlusion, and self-similarity. Given the wide baseline matches among images I have developed a novel method for Dynamic view morphing. Dynamic view morphing deals with the scenes containing moving objects in presence of camera motion. The objects can be rigid or non-rigid, each of them can move in any orientation or direction. The proposed method can generate a series of continuous and physically accurate intermediate views from only two reference images without any knowledge about 3D. The procedure consists of three steps: segmentation, morphing and post-warping. Given a boundary connection constraint, the source and target scenes are segmented into several layers for morphing. Based on the decomposition of affine transformation between corresponding points, we uniquely determine a physically correct path for post-warping by the least distortion method. I have successfully generalized the dynamic scene synthesis problem from the simple scene with only rotation to the dynamic scene containing non-rigid objects. My method can handle dynamic rigid or non-rigid objects, including complicated objects such as humans. Finally, I have also developed a novel algorithm for tri-view morphing. This is an efficient image-based method to navigate a scene based on only three wide-baseline un-calibrated images without the explicit use of a 3D model. After automatically recovering corresponding points between each pair of images using our wide baseline matching method, an accurate trifocal plane is extracted from the trifocal tensor implied in these three images. Next, employing a trinocular-stereo algorithm and barycentric blending technique, we generate an arbitrary novel view to navigate the scene in a 2D space. Furthermore, after self-calibration of the cameras, a 3D model can also be correctly augmented into this virtual environment synthesized by the tri-view morphing algorithm. We have applied our view morphing framework to several interesting applications: 4D video synthesis, automatic target recognition, multi-view morphing

Automatic 2D-to-3D conversion of single low depth-of-field images

This research presents a novel approach to the automatic rendering of 3D stereoscopic disparity image pairs from single 2D low depth-of-field (LDOF) images. Initially a depth map is produced through the assignment of depth to every delineated object and region in the image. Subsequently the left and right disparity images are produced through depth imagebased rendering (DIBR). The objects and regions in the image are initially assigned to one of six proposed groups or labels. Labelling is performed in two stages. The first involves the delineation of the dominant object-of-interest (OOI). The second involves the global object and region grouping of the non-OOI regions. The matting of the OOI is also performed in two stages. Initially the in focus foreground or region-of-interest (ROI) is separated from the out of focus background. This is achieved through the correlation of edge, gradient and higher-order statistics (HOS) saliencies. Refinement of the ROI is performed using k-means segmentation and CIEDE2000 colour-difference matching. Subsequently the OOI is extracted from within the ROI through analysis of the dominant gradients and edge saliencies together with k-means segmentation. Depth is assigned to each of the six labels by correlating Gestalt-based principles with vanishing point estimation, gradient plane approximation and depth from defocus (DfD). To minimise some of the dis-occlusions that are generated through the 3D warping sub-process within the DIBR process the depth map is pre-smoothed using an asymmetric bilateral filter. Hole-filling of the remaining dis-occlusions is performed through nearest-neighbour horizontal interpolation, which incorporates depth as well as direction of warp. To minimising the effects of the lateral striations, specific directional Gaussian and circular averaging smoothing is applied independently to each view, with additional average filtering applied to the border transitions. Each stage of the proposed model is benchmarked against data from several significant publications. Novel contributions are made in the sub-speciality fields of ROI estimation, OOI matting, LDOF image classification, Gestalt-based region categorisation, vanishing point detection, relative depth assignment and hole-filling or inpainting. An important contribution is made towards the overall knowledge base of automatic 2D-to-3D conversion techniques, through the collation of existing information, expansion of existing methods and development of newer concepts