836 research outputs found
The Comet Interceptor Mission
Here we describe the novel, multi-point Comet Interceptor mission. It is dedicated to the exploration of a little-processed long-period comet, possibly entering the inner Solar System for the first time, or to encounter an interstellar object originating at another star. The objectives of the mission are to address the following questions: What are the surface composition, shape, morphology, and structure of the target object? What is the composition of the gas and dust in the coma, its connection to the nucleus, and the nature of its interaction with the solar wind? The mission was proposed to the European Space Agency in 2018, and formally adopted by the agency in June 2022, for launch in 2029 together with the Ariel mission. Comet Interceptor will take advantage of the opportunity presented by ESAâs F-Class call for fast, flexible, low-cost missions to which it was proposed. The call required a launch to a halo orbit around the Sun-Earth L2 point. The mission can take advantage of this placement to wait for the discovery of a suitable comet reachable with its minimum ÎV capability of 600 msâ1. Comet Interceptor will be unique in encountering and studying, at a nominal closest approach distance of 1000 km, a comet that represents a near-pristine sample of material from the formation of the Solar System. It will also add a capability that no previous cometary mission has had, which is to deploy two sub-probes â B1, provided by the Japanese space agency, JAXA, and B2 â that will follow different trajectories through the coma. While the main probe passes at a nominal 1000 km distance, probes B1 and B2 will follow different chords through the coma at distances of 850 km and 400 km, respectively. The result will be unique, simultaneous, spatially resolved information of the 3-dimensional properties of the target comet and its interaction with the space environment. We present the missionâs science background leading to these objectives, as well as an overview of the scientific instruments, mission design, and schedule
Differentiable Display Photometric Stereo
Photometric stereo leverages variations in illumination conditions to
reconstruct per-pixel surface normals. The concept of display photometric
stereo, which employs a conventional monitor as an illumination source, has the
potential to overcome limitations often encountered in bulky and
difficult-to-use conventional setups. In this paper, we introduce
Differentiable Display Photometric Stereo (DDPS), a method designed to achieve
high-fidelity normal reconstruction using an off-the-shelf monitor and camera.
DDPS addresses a critical yet often neglected challenge in photometric stereo:
the optimization of display patterns for enhanced normal reconstruction. We
present a differentiable framework that couples basis-illumination image
formation with a photometric-stereo reconstruction method. This facilitates the
learning of display patterns that leads to high-quality normal reconstruction
through automatic differentiation. Addressing the synthetic-real domain gap
inherent in end-to-end optimization, we propose the use of a real-world
photometric-stereo training dataset composed of 3D-printed objects. Moreover,
to reduce the ill-posed nature of photometric stereo, we exploit the linearly
polarized light emitted from the monitor to optically separate diffuse and
specular reflections in the captured images. We demonstrate that DDPS allows
for learning display patterns optimized for a target configuration and is
robust to initialization. We assess DDPS on 3D-printed objects with
ground-truth normals and diverse real-world objects, validating that DDPS
enables effective photometric-stereo reconstruction
2022 GREAT Day Program
SUNY Geneseoâs Sixteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1016/thumbnail.jp
NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination
Recent advances in implicit neural representation have demonstrated the
ability to recover detailed geometry and material from multi-view images.
However, the use of simplified lighting models such as environment maps to
represent non-distant illumination, or using a network to fit indirect light
modeling without a solid basis, can lead to an undesirable decomposition
between lighting and material. To address this, we propose a fully
differentiable framework named neural ambient illumination (NeAI) that uses
Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in
a physically based way. Together with integral lobe encoding for
roughness-adaptive specular lobe and leveraging the pre-convoluted background
for accurate decomposition, the proposed method represents a significant step
towards integrating physically based rendering into the NeRF representation.
The experiments demonstrate the superior performance of novel-view rendering
compared to previous works, and the capability to re-render objects under
arbitrary NeRF-style environments opens up exciting possibilities for bridging
the gap between virtual and real-world scenes. The project and supplementary
materials are available at https://yiyuzhuang.github.io/NeAI/.Comment: Project page: <a class="link-external link-https"
href="https://yiyuzhuang.github.io/NeAI/" rel="external noopener
nofollow">https://yiyuzhuang.github.io/NeAI/</a
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Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project âSonic Palimpsestâ1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include womenâs voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Neural Reflectance Decomposition
Die Erstellung von fotorealistischen Modellen von Objekten aus Bildern oder Bildersammlungen ist eine grundlegende Herausforderung in der Computer Vision und Grafik. Dieses Problem wird auch als inverses Rendering bezeichnet. Eine der gröĂten Herausforderungen bei dieser Aufgabe ist die vielfĂ€ltige AmbiguitĂ€t. Der Prozess Bilder aus 3D-Objekten zu erzeugen wird Rendering genannt. Allerdings beeinflussen sich mehrere Eigenschaften wie Form, Beleuchtung und die ReflektivitĂ€t der OberflĂ€che gegenseitig. ZusĂ€tzlich wird eine Integration dieser EinflĂŒsse durchgefĂŒhrt, um das endgĂŒltige Bild zu erzeugen. Die Umkehrung dieser integrierten AbhĂ€ngigkeiten ist eine Ă€uĂerst schwierige und mehrdeutige Aufgabenstellung. Die Lösung dieser Aufgabe ist jedoch von entscheidender Bedeutung, da die automatisierte Erstellung solcher wieder beleuchtbaren Objekte verschiedene Anwendungen in den Bereichen Online-Shopping, Augmented Reality (AR), Virtual Reality (VR), Spiele oder Filme hat.
In dieser Arbeit werden zwei AnsĂ€tze zur Lösung dieser Aufgabe beschrieben. Erstens wird eine Netzwerkarchitektur vorgestellt, die die Erfassung eines Objekts und dessen Materialien von zwei Aufnahmen ermöglicht. Der Grad der Blicksynthese von diesen Objekten ist jedoch begrenzt, da bei der Dekomposition nur eine einzige Perspektive verwendet wird. Daher wird eine zweite Reihe von AnsĂ€tzen vorgeschlagen, bei denen eine Sammlung von 360 Grad verteilten Bildern in die Form, Reflektanz und Beleuchtung gespalten werden. Diese Multi-View-Bilder werden pro Objekt optimiert. Das resultierende Objekt kann direkt in handelsĂŒblicher Rendering-Software oder in Spielen verwendet werden. Wir erreichen dies, indem wir die aktuelle Forschung zu neuronalen Feldern erweitern Reflektanz zu speichern. Durch den Einsatz von Volumen-Rendering-Techniken können wir ein Reflektanzfeld aus natĂŒrlichen Bildsammlungen ohne jegliche Ground Truth (GT) Ăberwachung optimieren.
Die von uns vorgeschlagenen Methoden erreichen eine erstklassige QualitĂ€t der Dekomposition und ermöglichen neuartige Aufnahmesituationen, in denen sich Objekte unter verschiedenen Beleuchtungsbedingungen oder an verschiedenen Orten befinden können, was ĂŒblich fĂŒr Online-Bildsammlungen ist.Creating relightable objects from images or collections is a fundamental challenge in computer vision and graphics. This problem is also known as inverse rendering. One of the main challenges in this task is the high ambiguity. The creation of images from 3D objects is well defined as rendering. However, multiple properties such as shape, illumination, and surface reflectiveness influence each other. Additionally, an integration of these influences is performed to form the final image. Reversing these integrated dependencies is highly ill-posed and ambiguous. However, solving the task is essential, as automated creation of relightable objects has various applications in online shopping, augmented reality (AR), virtual reality (VR), games, or movies.
In this thesis, we propose two approaches to solve this task. First, a network architecture is discussed, which generalizes the decomposition of a two-shot capture of an object from large training datasets. The degree of novel view synthesis is limited as only a singular perspective is used in the decomposition. Therefore, the second set of approaches is proposed, which decomposes a set of 360-degree images. These multi-view images are optimized per object, and the result can be directly used in standard rendering software or games. We achieve this by extending recent research on Neural Fields, which can store information in a 3D neural volume. Leveraging volume rendering techniques, we can optimize a reflectance field from in-the-wild image collections without any ground truth (GT) supervision.
Our proposed methods achieve state-of-the-art decomposition quality and enable novel capture setups where objects can be under varying illumination or in different locations, which is typical for online image collections
2010 GREAT Day Program
SUNY Geneseoâs Fourth Annual GREAT Day.
This file has a supplement of three additional pages, linked in this record.https://knightscholar.geneseo.edu/program-2007/1004/thumbnail.jp
Biocatalytic Applications in Biotechnology
At present, the increasing demand for novel biotechnological products is supported through the continuous development of biocatalytic applications. As a consequence, the progress of research regarding enzymatic catalysis in aqueous, non-aqueous, organic (polar or non-polar), and/or non-solvent media is decisive. Experimental design methods, which also may comprise in silico studies, the design of specific reactors and conditions, the reactions of significant chemical and/or biochemical processes that are relevant to industrial production, enzyme kinetic methods, the investigation of enzymatic mechanisms and the use of immobilized enzymes and/or microbial cells on various inert matrices, are all useful. A plethora of enzymes of several classes, which may potentially be used as biocatalysts in biotechnological applications, are available. Among these enzymes, the more common are oxidoreductases (laccase, catalase, glucose oxidase, etc.), hydrolases (amylases, lipases, proteases, amidases, cellulases, esterases, etc.), isomerases (epimerases, topoisomerases, mutases, etc.), and others. By means of the aforementioned biocatalysts and the utilization of specific biotechnological methods, important, cost-effective, sustainable, and environmentally friendly processes have been applied for the synthesis and/or the conversion of a huge number of market-required products
Efficient image-based rendering
Recent advancements in real-time ray tracing and deep learning have significantly enhanced the realism of computer-generated images. However, conventional 3D computer graphics (CG) can still be time-consuming and resource-intensive, particularly when creating photo-realistic simulations of complex or animated scenes. Image-based rendering (IBR) has emerged as an alternative approach that utilizes pre-captured images from the real world to generate realistic images in real-time, eliminating the need for extensive modeling. Although IBR has its advantages, it faces challenges in providing the same level of control over scene attributes as traditional CG pipelines and accurately reproducing complex scenes and objects with different materials, such as transparent objects. This thesis endeavors to address these issues by harnessing the power of deep learning and incorporating the fundamental principles of graphics and physical-based rendering. It offers an efficient solution that enables interactive manipulation of real-world dynamic scenes captured from sparse views, lighting positions, and times, as well as a physically-based approach that facilitates accurate reproduction of the view dependency effect resulting from the interaction between transparent objects and their surrounding environment. Additionally, this thesis develops a visibility metric that can identify artifacts in the reconstructed IBR images without observing the reference image, thereby contributing to the design of an effective IBR acquisition pipeline. Lastly, a perception-driven rendering technique is developed to provide high-fidelity visual content in virtual reality displays while retaining computational efficiency.JĂŒngste Fortschritte im Bereich Echtzeit-Raytracing und Deep Learning haben den Realismus computergenerierter Bilder erheblich verbessert. Konventionelle 3DComputergrafik (CG) kann jedoch nach wie vor zeit- und ressourcenintensiv sein, insbesondere bei der Erstellung fotorealistischer Simulationen von komplexen oder animierten Szenen. Das bildbasierte Rendering (IBR) hat sich als alternativer Ansatz herauskristallisiert, bei dem vorab aufgenommene Bilder aus der realen Welt verwendet werden, um realistische Bilder in Echtzeit zu erzeugen, so dass keine umfangreiche Modellierung erforderlich ist. Obwohl IBR seine Vorteile hat, ist es eine Herausforderung, das gleiche MaĂ an Kontrolle ĂŒber Szenenattribute zu bieten wie traditionelle CG-Pipelines und komplexe Szenen und Objekte mit unterschiedlichen Materialien, wie z.B. transparente Objekte, akkurat wiederzugeben. In dieser Arbeit wird versucht, diese Probleme zu lösen, indem die Möglichkeiten des Deep Learning genutzt und die grundlegenden Prinzipien der Grafik und des physikalisch basierten Renderings einbezogen werden. Sie bietet eine effiziente Lösung, die eine interaktive Manipulation von dynamischen Szenen aus der realen Welt ermöglicht, die aus spĂ€rlichen Ansichten, Beleuchtungspositionen und Zeiten erfasst wurden, sowie einen physikalisch basierten Ansatz, der eine genaue Reproduktion des Effekts der SichtabhĂ€ngigkeit ermöglicht, der sich aus der Interaktion zwischen transparenten Objekten und ihrer Umgebung ergibt. DarĂŒber hinaus wird in dieser Arbeit eine Sichtbarkeitsmetrik entwickelt, mit der Artefakte in den rekonstruierten IBR-Bildern identifiziert werden können, ohne das Referenzbild zu betrachten, und die somit zur Entwicklung einer effektiven IBR-Erfassungspipeline beitrĂ€gt. SchlieĂlich wird ein wahrnehmungsgesteuertes Rendering-Verfahren entwickelt, um visuelle Inhalte in Virtual-Reality-Displays mit hoherWiedergabetreue zu liefern und gleichzeitig die Rechenleistung zu erhalten
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