thesis in 3d modeling

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Learning 3D Modeling and Simulation From and For the Real World

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Date issued, collections.

Interactive 3-D Modeling in Virtual Reality

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• Master of Science
• Computer Graphics Technology

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• West Lafayette

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Additional committee member 2, additional committee member 3, usage metrics.

• Virtual and mixed reality
• Computer graphics

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Dissertations / Theses on the topic '3D computer models'

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Zhu, Junyi S. M. Massachusetts Institute of Technology. "A software pipeline for converting 3D models into 3D breadboards." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122732.

Rasmus, Siljedahl. "3D Conversion from CAD models to polygon models." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129881.

Bici, Mehmet Oguz. "Robust Transmission Of 3d Models." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612690/index.pdf.

Magnusson, Henrik. "Integrated generic 3D visualization of Modelica models." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15453.

OpenModelica is a complete environment for developing and simulatingModelica models based on free software. It is promoted and developed bythe OpenModelica Consortium. This thesis details a method for describingand consequently displaying visualizations of Modelica models in OMNote-book, an application in the OpenModelica suite where models can be writtenand simulated in a document mixed with text, images and plots. Two dif-ferent approaches are discussed; one based on Modelica annotations and onebased on creating a simple object hierarchy which can be connected to exist-ing models. Trial implementations are done which make it possible to discardthe annotation approach, and show that an object based solution is the onebest suited for a complete implementation. It is expanded into a working 3Dvisualization solution, embedded in OMNotebook.

Aubry, Mathieu. "Representing 3D models for alignment and recognition." Thesis, Paris, Ecole normale supérieure, 2015. http://www.theses.fr/2015ENSU0006/document.

Gil, Camacho Carlos. "Part Detection in Oneline-Reconstructed 3D Models." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-51600.

Buchholz, Henrik. "Real-time visualization of 3D city models." Phd thesis, Universität Potsdam, 2006. http://opus.kobv.de/ubp/volltexte/2007/1333/.

Shlyakhter, Ilya 1975, and Max 1976 Rozenoer. "Reconstruction of 3D tree models from instrumented photographs." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80136.

Huang, Jennifer 1980. "Component-based face recognition with 3D morphable models." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87403.

Marks, Tim K. "Facing uncertainty 3D face tracking and learning with generative models /." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3196545.

Lindberg, Mimmi. "Forensic Validation of 3D models." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171159.

Simek, Kyle. "Branching Gaussian Process Models for Computer Vision." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612094.

Zhao, Shu Jie. "Line drawings abstraction from 3D models." Thesis, University of Macau, 2009. http://umaclib3.umac.mo/record=b2130124.

BHAT, RAVINDRA K. "VISUALIZATION USING COMPUTER GENERATED 3D MODELS AND THEIR APPLICATIONS IN ARCHITECTURAL PRACTICE." The University of Arizona, 1994. http://hdl.handle.net/10150/555416.

Bayar, Hakan. "Texture Mapping By Multi-image Blending For 3d Face Models." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609063/index.pdf.

Duval, Thierry. "Models for design, implementation and deployment of 3D Collaborative Virtual Environments." Habilitation à diriger des recherches, Université Rennes 1, 2012. http://tel.archives-ouvertes.fr/tel-00764830.

Weaver, Alexandra Alden. "Gaming Reality: Real-World 3D Models in Interactive Media." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/scripps_theses/619.

Sundberg, Simon. "Evolving digital 3D models using interactive genetic algorithm." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177580.

Yao, Miaojun. "3D Printable Designs of Rigid and Deformable Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1502906675481174.

Villanueva, Aylagas Mónica. "Reconstruction and recommendation of realistic 3D models using cGANs." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231492.

Mazzolini, Ryan. "Procedurally generating surface detail for 3D models using voxel-based cellular automata." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20502.

Trapp, Matthias. "Analysis and exploration of virtual 3D city models using 3D information lenses." Master's thesis, Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2008/1393/.

Chen, Xiaochen. "Tracking vertex flow on 3D dynamic facial models." Diss., Online access via UMI:, 2008.

Eriksson, Oliver, and William Lindblom. "Comparing Perception of Animated Imposters and 3D Models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280333.

Orriols, Majoral Xavier. "Generative Models for Video Analysis and 3D Range Data Applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2004. http://hdl.handle.net/10803/3037.

Waldow, Walter E. "An Adversarial Framework for Deep 3D Target Template Generation." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1597334881614898.

Glander, Tassilo. "Multi-scale representations of virtual 3D city models." Phd thesis, Universität Potsdam, 2012. http://opus.kobv.de/ubp/volltexte/2013/6411/.

Rosato, Matthew J. "Applying conformal mapping to the vertex correspondence problem for 3D face models." Diss., Online access via UMI:, 2007.

Mao, Bo. "Visualisation and Generalisation of 3D City Models." Licentiate thesis, KTH, Geoinformatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-24345.

3D city models have been widely used in different applications such as urban planning, traffic control, disaster management etc. Effective visualisation of 3D city models in various scales is one of the pivotal techniques to implement these applications. In this thesis, a framework is proposed to visualise the 3D city models both online and offline using City Geography Makeup Language (CityGML) and Extensible 3D (X3D) to represent and present the models. Then, generalisation methods are studied and tailored to create 3D city scenes in multi-scale dynamically. Finally, the quality of generalised 3D city models is evaluated by measuring the visual similarity from the original models.

In the proposed visualisation framework, 3D city models are stored in CityGML format which supports both geometric and semantic information. These CityGML files are parsed to create 3D scenes and be visualised with existing 3D standard. Because the input and output in the framework are all standardised, it is possible to integrate city models from different sources and visualise them through the different viewers.

Considering the complexity of the city objects, generalisation methods are studied to simplify the city models and increase the visualisation efficiency. In this thesis, the aggregation and typification methods are improved to simplify the 3D city models.

Multiple representation data structures are required to store the generalisation information for dynamic visualisation. One of these is the CityTree, a novel structure to represent building group, which is tested for building aggregation. Meanwhile, Minimum Spanning Tree (MST) is employed to detect the linear building group structures in the city models and they are typified with different strategies. According to the experiments results, by using the CityTree, the generalised 3D city model creation time is reduced by more than 50%.

Different generalisation strategies lead to different outcomes. It is important to evaluate the quality of the generalised models. In this thesis a new evaluation method is proposed: visual features of the 3D city models are represented by Attributed Relation Graph (ARG) and their similarity distances are calculated with Nested Earth Mover’s Distance (NEMD) algorithm. The calculation results and user survey show that the ARG and NEMD methods can reflect the visual similarity between generalised city models and the original ones.

Berg, Martin. "Pose Recognition for Tracker Initialization Using 3D Models." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11080.

In this thesis it is examined whether the pose of an object can be determined by a system trained with a synthetic 3D model of said object. A number of variations of methods using P-channel representation are examined. Reference images are rendered from the 3D model, features, such as gradient orientation and color information are extracted and encoded into P-channels. The P-channel representation is then used to estimate an overlapping channel representation, using B 1 -spline functions, to estimate a density function for the feature set. Experiments were conducted with this representation as well as the raw P-channel representation in conjunction with a number of distance measures and estimation methods.

It is shown that, with correct preprocessing and choice of parameters, the pose can be detected with some accuracy and, if not in real-time, fast enough to be useful in a tracker initialization scenario. It is also concluded that the success rate of the estimation depends heavily on the nature of the object.

Vandeventer, Jason. "4D (3D Dynamic) statistical models of conversational expressions and the synthesis of highly-realistic 4D facial expression sequences." Thesis, Cardiff University, 2015. http://orca.cf.ac.uk/82405/.

Kim, Taewoo. "A 3D XML-based modeling and simulation framework for dynamic models." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1000134.

Reichert, Thomas. "Development of 3D lattice models for predicting nonlinear timber joint behaviour." Thesis, Edinburgh Napier University, 2009. http://researchrepository.napier.ac.uk/Output/2827.

Mutapcic, Emir. "Optimised part programs for excimer laser-ablation micromachining directly from 3D CAD models." Australian Digital Thesis Program, 2006. http://adt.lib.swin.edu.au/public/adt-VSWT20061117.154651.

Svensson, Stina. "Representing and analyzing 3D digital shape using distance information /." Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 2001. http://epsilon.slu.se/avh/2001/91-576-6095-6.pdf.

Baecher, Moritz Niklaus. "From Digital to Physical: Computational Aspects of 3D Manufacturing." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11149.

Chiu, Wai-kei, and 趙偉奇. "Hollowing and reinforcing 3D CAD models and representing multiple material objects for rapid prototyping." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B29768470.

Chiu, Wai-kei. "Hollowing and reinforcing 3D CAD models and representing multiple material objects for rapid prototyping /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22050498.

Smith, Robert L. M. Eng Massachusetts Institute of Technology. "Afterimage Toon Blur : procedural generation of cartoon blur for 3D models in real time." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106376.

Wang, Changling. "Sketch based 3D freeform object modeling with non-manifold data structure /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?MECH%202002%20WANGC.

Zouhar, Marek. "Tvorba a demonstrace 3D modelů pro VR." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363852.

Hittner, Brian Edward. "Rendering large-scale terrain models and positioning objects in relation to 3D terrain." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FHittner.pdf.

ARMENISE, VALENTINA. "Estimation of the 3D Pose of objects in a scenecaptured with Kinect camera using CAD models." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142471.

Lorenz, Haik. "Texturierung und Visualisierung virtueller 3D-Stadtmodelle." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5387/.

Guo, Jinjiang. "Contributions to objective and subjective visual quality assessment of 3d models." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI099.

Mian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition." University of Western Australia. School of Computer Science and Software Engineering, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0046.

Brown, Steven W. "Interactive Part Selection for Mesh and Point Models Using Hierarchical Graph-cut Partitioning." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2420.pdf.

Roussellet, Valentin. "Implicit muscle models for interactive character skinning." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30055/document.

Al-Douri, Firas A. Salman. "Impact of utilizing 3D digital urban models on the design content of urban design plans in US cities." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4324.

Abayowa, Bernard Olushola. "Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of Large Scale City Models." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1372508452.

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Quantitative Biology > Biomolecules

Title: mixed continuous and categorical flow matching for 3d de novo molecule generation.

Abstract: Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Diffusion models currently achieve state of the art performance for 3D molecule generation. In this work, we explore the use of flow matching, a recently proposed generative modeling framework that generalizes diffusion models, for the task of de novo molecule generation. Flow matching provides flexibility in model design; however, the framework is predicated on the assumption of continuously-valued data. 3D de novo molecule generation requires jointly sampling continuous and categorical variables such as atom position and atom type. We extend the flow matching framework to categorical data by constructing flows that are constrained to exist on a continuous representation of categorical data known as the probability simplex. We call this extension SimplexFlow. We explore the use of SimplexFlow for de novo molecule generation. However, we find that, in practice, a simpler approach that makes no accommodations for the categorical nature of the data yields equivalent or superior performance. As a result of these experiments, we present FlowMol, a flow matching model for 3D de novo generative model that achieves improved performance over prior flow matching methods, and we raise important questions about the design of prior distributions for achieving strong performance in flow matching models. Code and trained models for reproducing this work are available at this https URL

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FACT SHEET: DHS Facilitates the Safe and Responsible Deployment and Use of Artificial Intelligence in Federal Government, Critical Infrastructure, and U.S. Economy

The Department of Homeland Security (DHS) plays a leading role in developing policies and introducing initiatives to help ensure the safe and secure use of AI in support of President Biden’s landmark Executive Order (EO). DHS is responsibly leveraging AI to advance homeland security mission, while protecting individuals’ privacy, civil rights, and civil liberties. DHS is helping to secure the nation’s critical infrastructure from AI-enabled threats, issuing safety and security guidelines for AI used in critical systems, and recruiting the talent needed to shape the future of AI and tech innovation.

To learn more about DHS’s work in AI, visit the Artificial Intelligence at DHS webpage.

Protecting water supplies, power grids, telecommunications and other critical infrastructure from AI-enabled threats

• Established the AI Safety and Security Board (AISSB) – Over 20 technology and critical infrastructure executives, civil rights leaders, academics, and policymakers are advising the Secretary, the critical infrastructure community, private sector stakeholders, and the broader public on the safe and secure development and deployment of AI in our nation’s critical infrastructure, as well as the mitigation of threats and risks that AI could pose. This includes recommendations to prevent and prepare for AI-related disruptions to critical services that impact national or economic security, public health, or safety.  
• Developed the First AI Guidelines for Critical Infrastructure Owners and Operators – DHS, in partnership with CISA, developed new safety and security guidelines for use by critical infrastructure owners and operators. These guidelines are informed by the whole-of-government effort to assess AI risks across all sixteen critical infrastructure sectors, and address threats both to and from, and involving AI systems.  
• Shared New Resources to Reduce Risks at the Intersection of AI and Chemical, Biological, Radiological, and Nuclear Threats –The AI Chemical, biological, radiological, and nuclear (CBRN) Report [submitted to the President] was developed through strong collaboration across government, academia, and industry. The report examines risks at the intersection of AI and CBRN threats.  It also identifies trends in AI and types of AI models that might present or intensify biological and chemical threats to the U.S. Finally, the report offers recommendations to mitigate potential threats to national security by overseeing the training, deployment, publication, and use of AI models and underlying data, including the role of safety evaluations and guardrails.

Incorporating AI into Carrying Out the Homeland Security mission through safe, responsible and trustworthy use

• Responsibly leverage AI to advance Homeland Security missions while protecting individuals’ privacy, civil rights, and civil liberties;
• Promote Nationwide AI Safety and Security; and
• Continue to lead in AI through strong cohesive partnerships  
• Per the EO, CISA completed an operational pilot using AI cybersecurity systems to aid in the detection and remediation of vulnerabilities in critical United States Government software, systems, and networks.
• Homeland Security Investigations (HSI) will use AI to enhance investigative processes focused on detecting fentanyl and increasing efficiency of investigations related to combatting child sexual exploitation.
• The Federal Emergency Management Agency (FEMA) will deploy AI to help communities plan for and develop hazard mitigation plans to build resilience and minimize risks.
• United States Citizenship and Immigration Services (USCIS) will use AI to improve immigration officer training.  
• Launched the AI Corps Recruitment Sprint to Hire 50 Experienced/Expert Technologists – The Department announced its first-ever hiring sprint to recruit 50 AI technology experts in 2024. The new DHS “AI Corps” is modeled after the U.S. Digital Service, building teams that will help better leverage this new technology responsibly across strategic areas of the homeland security enterprise including efforts to counter fentanyl, combat child sexual exploitation and abuse, deliver immigration services, secure travel, fortify our critical infrastructure, and enhance our cybersecurity. The Department most recently hired a highly qualified executive director to ensure that the Corps' success and is continuing to pursue top AI talent to build out the AI Corps.

Ensuring the economic security, and competitiveness of American AI innovation

• HSI has partnered with Michigan State University’s Center for Anti-Counterfeiting and Product Protection (A-CAPP) to create a training program to help industry and domestic law enforcement better understand and respond to AI-related IP theft.
• The Intellectual Property Rights Center (IPR Center) , led by HSI, encourages members of the public, industry, trade associations, law enforcement, and government agencies to report potential violations of intellectual property rights involving AI through their website. The information provided is then reviewed promptly by IPR Center staff and disseminated for appropriate investigative response and tactical use to IPR Center partners. The IPR center serves as whole of government center for the criminal enforcement of IP theft, to include AI-enabled digital piracy, product counterfeiting, trade fraud, and the theft of trade secrets.  
• Streamlined processing times of petitions and applications for those seeking to work, study, or conduct research in the United States;
• O-1A noncitizens of extraordinary ability,
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• EB-2 advanced-degree holders and noncitizens of exceptional ability,
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• International students.  
• Implemented a final rule to strengthen the integrity of the H-1B registration process and published a comprehensive proposed rule to modernize the H-1B specialty occupation worker program and enhance its integrity and usage.

As DHS deploys AI responsibly, it will work under the President’s direction to harness the opportunities and reduce the potential harms of this revolutionary technology.

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Moving Pictures: Transform Images Into 3D Scenes With NVIDIA Instant NeRF

Editor’s note: This post is part of the AI Decoded series , which demystifies AI by making the technology more accessible, and which showcases new hardware, software, tools and accelerations for RTX PC users.

Imagine a gorgeous vista, like a cliff along the water’s edge. Even as a 2D image, the scene would be beautiful and inviting. Now imagine exploring that same view in 3D – without needing to be there.

NVIDIA RTX technology-powered AI helps turn such an imagination into reality. Using Instant NeRF , creatives are transforming collections of still images into digital 3D scene s in just seconds.

Simply Radiant

A NeRF, or neural radiance field, is an AI model that takes 2D images representing a scene as input and interpolates between them to render a complete 3D scene. The model operates as a neural network — a model that replicates how the brain is organized and is often used for tasks that require pattern recognition.

Using spatial location and volumetric rendering, a NeRF uses the camera pose from the images to render a 3D iteration of the scene. Traditionally, these models are computationally intensive, requiring large amounts of rendering power and time.

A recent NVIDIA AI research project changed that.

Instant NeRF takes NeRFs to the next level, using AI-accelerated inverse rendering to approximate how light behaves in the real world. It enables researchers to construct a 3D scene from 2D images taken at different angles. Scenes can now be generated in seconds, and the longer the NeRF model is trained, the more detailed the resulting 3D renders.

NVIDIA researchers released four neural graphics primitives, or pretrained datasets, as part of the Instant-NGP training toolset at the SIGGRAPH computer graphics conference in 2022. The tools let anyone create NeRFs with their own data. The researchers won a best paper award for the work, and TIME Magazine named Instant NeRF a best invention of 2022 .

In addition to speeding rendering for NeRFs, Instant NeRF makes the entire image reconstruction process accessible using NVIDIA RTX and GeForce RTX desktop and laptop GPUs. While the time it takes to render a scene depends on factors like dataset size and the mix of image and video source content, the AI training doesn’t require server-grade or cloud-based hardware.

NVIDIA RTX workstations and GeForce RTX PCs are ideally suited to meet the computational demands of rendering NeRFs. NVIDIA RTX and GeForce RTX GPUs feature Tensor Cores, dedicated AI hardware accelerators that provide the horsepower to run generative AI locally.

Ready, Set, Go

Get started with Instant NeRF to learn about radiance fields and experience imagery in a new way.

Developers and tech enthusiasts can download the source-code base to compile. Nontechnical users can download the Windows installers for Instant-NGP software, available on GitHub .

While the installer is available for a wide range of RTX GPUs, the program performs best on the latest-architecture GeForce RTX 40 Series and NVIDIA RTX Ada Generation GPUs.

The “Getting Started With Instant NeRF” guide walks users through the process, including loading one of the primitives, such as “NeRF Fox,” to get a sense of what’s possible. Detailed instructions and video walkthroughs — like the one above — demonstrate how to create NeRFs with custom data, including tips for capturing good input imagery and compiling codebases (if built from source). The guide also covers using the Instant NeRF graphical user interface, optimizing scene parameters and creating an animation from the scene.

The NeRF community also offers many tips and tricks to help users get started. For example, check out the livestream below and this technical blog post .

Show and Tell

Digital artists are composing beautiful scenes and telling fresh stories with NVIDIA Instant NeRF. The Instant NeRF gallery showcases some of the most innovative and thought-provoking examples, viewable as video clips in any web browser.

Here are a few:

• “Through the Looking Glass” by Karen X. Cheng and James Perlman A pianist practices her song, part of her daily routine, though there’s nothing mundane about what happens next. The viewer peers into the mirror, a virtual world that can be observed but not traversed; it’s unreachable by normal means. Then, crossing the threshold, it’s revealed that this mirror is in fact a window into an inverted reality that can be explored from within. Which one is real?
• “Meditation” by Franc Lucent As soon as they walked into one of many rooms in Nico Santucci’s estate, Lucent knew they needed to turn it into a NeRF. Playing with the dynamic range and reflections in the pond, it presented the artist with an unknown exploration. They were pleased with the softness of the light and the way the NeRF elevates the room into what looks like something out of a dream — the perfect place to meditate. A NeRF can freeze a moment in a way that’s more immersive than a photo or video.
• “Zeus” by Hugues Bruyère These rendered 3D scenes with Instant NeRF use the data Bruyère previously captured for traditional photogrammetry using mirrorless digital cameras, smartphones, 360-degree cameras and drones. Instant NeRF gives him a powerful tool to help preserve and share cultural artifacts through online libraries, museums, virtual-reality experiences and heritage-conservation projects. This NeRF was trained using a dataset of photos taken with an iPhone at the Royal Ontario Museum.

From Image to Video to Reality

Transforming images into a 3D scene with AI is cool. Stepping into that 3D creation is next level.

Thanks to a recent Instant NeRF update, users can render their scenes from static images and virtually step inside the environments, moving freely within the 3D space. In virtual-reality (VR) environments, users can feel complete immersion into new worlds, all within their headsets.

The potential benefits are nearly endless.

For example, a realtor can create and share a 3D model of a property, offering virtual tours at new levels. Retailers can showcase products in an online shop, powered by a collection of images and AI running on RTX GPUs. These AI models power creativity and are helping drive the accessibility of 3D immersive experiences across other industries.

Instant NeRF comes with the capability to clean up scenes easily in VR, making the creation of high-quality NeRFs more intuitive than ever. Learn more about navigating Instant NeRF spaces in VR .

Download Instant-NGP to get started, and share your creations on social media with the hashtag #InstantNeRF.

Generative AI is transforming gaming, videoconferencing and interactive experiences of all kinds. Make sense of what’s new and what’s next by subscribing to the AI Decoded newsletter .

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