3,451 research outputs found
OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas.
Recent work on depth estimation up to now has only focused on projective images ignoring 360o content which is now increasingly and more easily produced. We show that monocular depth estimation models trained on traditional images produce sub-optimal results on omnidirectional images, showcasing the need for training directly on 360o datasets, which however, are hard to acquire. In this work, we circumvent the challenges associated with acquiring high quality 360o datasets with ground truth depth annotations, by re-using recently released large scale 3D datasets and re-purposing them to 360o via rendering. This dataset, which is considerably larger than similar projective datasets, is publicly offered to the community to enable future research in this direction. We use this dataset to learn in an end-to-end fashion the task of depth estimation from 360o images. We show promising results in our synthesized data as well as in unseen realistic images
The Maunakea Spectroscopic Explorer Book 2018
(Abridged) This is the Maunakea Spectroscopic Explorer 2018 book. It is
intended as a concise reference guide to all aspects of the scientific and
technical design of MSE, for the international astronomy and engineering
communities, and related agencies. The current version is a status report of
MSE's science goals and their practical implementation, following the System
Conceptual Design Review, held in January 2018. MSE is a planned 10-m class,
wide-field, optical and near-infrared facility, designed to enable
transformative science, while filling a critical missing gap in the emerging
international network of large-scale astronomical facilities. MSE is completely
dedicated to multi-object spectroscopy of samples of between thousands and
millions of astrophysical objects. It will lead the world in this arena, due to
its unique design capabilities: it will boast a large (11.25 m) aperture and
wide (1.52 sq. degree) field of view; it will have the capabilities to observe
at a wide range of spectral resolutions, from R2500 to R40,000, with massive
multiplexing (4332 spectra per exposure, with all spectral resolutions
available at all times), and an on-target observing efficiency of more than
80%. MSE will unveil the composition and dynamics of the faint Universe and is
designed to excel at precision studies of faint astrophysical phenomena. It
will also provide critical follow-up for multi-wavelength imaging surveys, such
as those of the Large Synoptic Survey Telescope, Gaia, Euclid, the Wide Field
Infrared Survey Telescope, the Square Kilometre Array, and the Next Generation
Very Large Array.Comment: 5 chapters, 160 pages, 107 figure
Learning 3D Scene Priors with 2D Supervision
Holistic 3D scene understanding entails estimation of both layout
configuration and object geometry in a 3D environment. Recent works have shown
advances in 3D scene estimation from various input modalities (e.g., images, 3D
scans), by leveraging 3D supervision (e.g., 3D bounding boxes or CAD models),
for which collection at scale is expensive and often intractable. To address
this shortcoming, we propose a new method to learn 3D scene priors of layout
and shape without requiring any 3D ground truth. Instead, we rely on 2D
supervision from multi-view RGB images. Our method represents a 3D scene as a
latent vector, from which we can progressively decode to a sequence of objects
characterized by their class categories, 3D bounding boxes, and meshes. With
our trained autoregressive decoder representing the scene prior, our method
facilitates many downstream applications, including scene synthesis,
interpolation, and single-view reconstruction. Experiments on 3D-FRONT and
ScanNet show that our method outperforms state of the art in single-view
reconstruction, and achieves state-of-the-art results in scene synthesis
against baselines which require for 3D supervision.Comment: Video: https://youtu.be/YT7MEdygRoY Project:
https://yinyunie.github.io/sceneprior-page
Artistic Path Space Editing of Physically Based Light Transport
Die Erzeugung realistischer Bilder ist ein wichtiges Ziel der Computergrafik, mit Anwendungen u.a. in der Spielfilmindustrie, Architektur und Medizin. Die physikalisch basierte Bildsynthese, welche in letzter Zeit anwendungsübergreifend weiten Anklang findet, bedient sich der numerischen Simulation des Lichttransports entlang durch die geometrische Optik vorgegebener Ausbreitungspfade; ein Modell, welches für übliche Szenen ausreicht, Photorealismus zu erzielen.
Insgesamt gesehen ist heute das computergestützte Verfassen von Bildern und Animationen mit wohlgestalteter und theoretisch fundierter Schattierung stark vereinfacht. Allerdings ist bei der praktischen Umsetzung auch die Rücksichtnahme auf Details wie die Struktur des Ausgabegeräts wichtig und z.B. das Teilproblem der effizienten physikalisch basierten Bildsynthese in partizipierenden Medien ist noch weit davon entfernt, als gelöst zu gelten.
Weiterhin ist die Bildsynthese als Teil eines weiteren Kontextes zu sehen: der effektiven Kommunikation von Ideen und Informationen. Seien es nun Form und Funktion eines Gebäudes, die medizinische Visualisierung einer Computertomografie oder aber die Stimmung einer Filmsequenz -- Botschaften in Form digitaler Bilder sind heutzutage omnipräsent. Leider hat die Verbreitung der -- auf Simulation ausgelegten -- Methodik der physikalisch basierten Bildsynthese generell zu einem Verlust intuitiver, feingestalteter und lokaler künstlerischer Kontrolle des finalen Bildinhalts geführt, welche in vorherigen, weniger strikten Paradigmen vorhanden war.
Die Beiträge dieser Dissertation decken unterschiedliche Aspekte der Bildsynthese ab. Dies sind zunächst einmal die grundlegende Subpixel-Bildsynthese sowie effiziente Bildsyntheseverfahren für partizipierende Medien. Im Mittelpunkt der Arbeit stehen jedoch Ansätze zum effektiven visuellen Verständnis der Lichtausbreitung, die eine lokale künstlerische Einflussnahme ermöglichen und gleichzeitig auf globaler Ebene konsistente und glaubwürdige Ergebnisse erzielen. Hierbei ist die Kernidee, Visualisierung und Bearbeitung des Lichts direkt im alle möglichen Lichtpfade einschließenden "Pfadraum" durchzuführen. Dies steht im Gegensatz zu Verfahren nach Stand der Forschung, die entweder im Bildraum arbeiten oder auf bestimmte, isolierte Beleuchtungseffekte wie perfekte Spiegelungen, Schatten oder Kaustiken zugeschnitten sind. Die Erprobung der vorgestellten Verfahren hat gezeigt, dass mit ihnen real existierende Probleme der Bilderzeugung für Filmproduktionen gelöst werden können
State of the Art on Neural Rendering
Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo-realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state-of-the-art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photo-realistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. This state-of-the-art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems
\u3cem\u3eGRASP News\u3c/em\u3e, Volume 6, Number 1
A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory, edited by Gregory Long and Alok Gupta
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