Tech
AI method reconstructs 3D scene details from simulated images using inverse rendering

Over the past decades, computer scientists have developed many computational tools that can analyze and interpret images. These tools have proved useful for a broad range of applications, including robotics, autonomous driving, health care, manufacturing and even entertainment.
Most of the best performing computer vision approaches employed to date rely on so-called feed-forward neural networks. These are computational models that process input images step by step, ultimately making predictions about them.
While some of these models were found to perform well when tested on the data they analyzed during training, they often do not generalize well across new images and in different scenarios. In addition, their predictions and the patterns they extract from images can be difficult to interpret.
Researchers at Princeton University recently developed a new inverse rendering approach that is more transparent and could also interpret a wide range of images more reliably. The new approach, introduced in a paper published in Nature Machine Intelligence, relies on a generative artificial intelligence (AI)-based method to simulate the process of image creation, while also optimizing it by gradually adjusting a model’s internal parameters.
“Generative AI and neural rendering have transformed the field in recent years for creating novel content: producing images or videos from scene descriptions,” Felix Heide, senior author of the paper, told Tech Xplore. “We investigate whether we can flip this around and use these generative models for extracting the scene descriptions from images.”
The new approach developed by Heide and his colleagues relies on a so-called differentiable rendering pipeline. This is a process for the simulation of image creation, relying on compressed representations of images created by generative AI models.
“We developed an analysis-by-synthesis approach that allows us to solve vision tasks, such as tracking, as test-time optimization problems,” explained Heide. “We found that this method generalizes across datasets, and in contrast to existing supervised learning methods, does not need to be trained on new datasets.”
Essentially, the method developed by the researchers works by placing models of 3D objects in a virtual scene depicting real world settings. These models of objects are generated by a generative AI based on random sample of 3D scene parameters.
“We then render all these objects back together into a 2D image,” said Heide. “Next, we compare this rendered image with the real observed image. Based on how different they are, we backpropagate the difference through both the differentiable rendering function and the 3D generation model to update its inputs. In just a few steps, we optimize these inputs to make the rendered match the observed images better.”
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Optimizing 3D models through inverse neural rendering. From left to right: the observed image, initial random 3D generations, and three optimization steps that refine these to better match the observed image. The observed images are faded to show the rendered objects clearly. The method effectively refines object appearance and position, all done at test time with inverse neural rendering. Credit: Ost et al.
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Generalization of 3D multi-object tracking with Inverse Neural Rendering. The method directly generalizes across datasets such as the nuScenes and Waymo Open Dataset benchmarks without additional fine-tuning and is trained on synthetic 3D models only. The observed images are overlaid with the closest generated object and tracked 3D bounding boxes. Credit: Ost et al.
A notable advantage of the team’s newly proposed approach is that it allows very generic 3D object generation models trained on synthetic data to perform well across a wide range of datasets containing images captured in real-world settings. In addition, the renderings produced by the models are far more explainable than those produced by conventional rendering tools based on feed-forward machine learning models.
“Our inverse rendering approach for tracking works just as well as learned feed-forward approaches, but it provides us with explicit 3D explanations of its perceived world,” said Heide.
“The other interesting aspect is the generalization capabilities. Without changing the 3D generation model or training it on new data, our 3D multi-object tracking through Inverse Neural Rendering works well across different autonomous driving datasets and object types. This can significantly reduce the cost of fine-tuning on new data or at least work as an auto-labeling pipeline.”
This recent study could soon help to advance AI models for computer vision, improving their performance in real-world settings while also increasing their transparency. The researchers now plan to continue improving their method and start testing it on more computer vision-related tasks.
“A logical next step is the expansion of the proposed approach to other perception tasks, such as 3D detection and 3D segmentation,” added Heide. “Ultimately, we want to explore if inverse rendering can even be used to infer the whole 3D scene, and not just individual objects. This would allow our future robots to reason and continuously optimize a three-dimensional model of the world, which comes with built-in explainability.”
Written for you by our author Ingrid Fadelli,
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More information:
Julian Ost et al, Towards generalizable and interpretable three-dimensional tracking with inverse neural rendering, Nature Machine Intelligence (2025). DOI: 10.1038/s42256-025-01083-x.
© 2025 Science X Network
Citation:
AI method reconstructs 3D scene details from simulated images using inverse rendering (2025, August 23)
retrieved 23 August 2025
from https://techxplore.com/news/2025-08-ai-method-reconstructs-3d-scene.html
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Tech
Manufacturas Eliot boosts digital shift with Coats Digital’s VisionPLM

Coats Digital is pleased to announce that that Manufacturas Eliot, one of Colombia’s leading fashion textile groups, has selected VisionPLM to advance its digital transformation strategy. The solution will optimise product lifecycle management across its portfolio of brands—Patprimo, Seven Seven, Ostu, and Atmos—enhancing collaboration, streamlining operations, and enabling greater speed to market.
Manufacturas Eliot, a Colombian fashion group, has selected Coats Digital’s VisionPLM to boost digital transformation across its brands.
The platform will enhance collaboration, speed up product development, and streamline operations.
VisionPLM aims to improve agility, traceability, and decision-making, supporting Eliot’s drive for innovation and sustainable growth.
Founded in 1957, Manufacturas Eliot is a vertically integrated manufacturer producing over 20 million garments annually. Renowned for delivering high-quality, accessible fashion, the group continues to invest in technologies that support sustainable growth and operational excellence.
The implementation of VisionPLM demonstrates Elliot’s strong commitment to end-to-end digitalisation across the value chain. By introducing VisionPLM, Eliot aims to improve product development agility, reduce time-to-market, and ensure seamless communication across cross-functional teams.
Juliana Pérez, Design Director, Seven Seven, commented: “From the design team’s point of view, we’re really excited about implementing VisionPLM, as it will allow us to manage our collections in a more structured way and collaborate efficiently with other departments.”
Angela Quevedo, Planning Director, Manufacturas Eliot, added: “VisionPLM will significantly improve the planning and coordination of our operations by enabling a more accurate flow of information and reducing response times across the supply chain. It will also help us optimise processes and accelerate decision-making.”
Tailored specifically for the fashion industry, VisionPLM integrates tools that boost development speed, improve traceability, and enhance decision-making. By centralising design, sourcing, and supplier collaboration in one digital platform, the solution enables a streamlined, transparent, and responsive approach to managing collections.
Oscar González, Coats Digital – LATAM, said: “We’re proud to continue supporting Manufacturas Eliot on its digital transformation journey. The adoption of VisionPLM marks a key milestone in advancing its fashion innovation strategy—enabling faster, smarter decision-making and more agile collaboration across teams and suppliers. Its helping to build a future-ready, connected operation that’s fully aligned to the demands of today’s fashion market.”
Note: The headline, insights, and image of this press release may have been refined by the Fibre2Fashion staff; the rest of the content remains unchanged.
Fibre2Fashion News Desk (HU)
Tech
Top CDC Officials Resign After Director Is Pushed Out

Susan Monarez is no longer the director of the US Centers for Disease Control and Prevention, according to a post by the official Department of Health and Human Services X account. She had been in the position for just a month. In the wake of her apparent ouster, several other CDC leaders have resigned.
Named acting CDC director in January, Monarez was officially confirmed to the position by the Senate on July 29 and sworn in two days later. During her brief tenure, the CDC’s main campus in Atlanta was attacked by a gunman who blamed the Covid-19 vaccine for making him sick and depressed. A local police officer, David Rose, was killed by the suspect when responding to the shooting.
In a statement Wednesday evening Mark Zaid and Abbe David Lowell, Monarez’s lawyers, alleged that she had been “targeted” for refusing “to rubber-stamp unscientific, reckless directives and fire dedicated health experts.” The statement further says that Monarez has not resigned and does not plan to, and claims that she has not received notification that she’s been fired.
According to emails obtained by WIRED, at least three other senior CDC officials resigned Wednesday evening: Demetre Daskalakis, director of the National Center for Immunization and Respiratory Diseases; Debra Houry, chief medical officer and deputy director for program and science; and Daniel Jernigan, director of the National Center for Emerging and Zoonotic Infectious Diseases.
More resignations are expected to become public soon, say CDC with knowledge of the departures.
“I worry that political appointees will not make decisions on the science, but instead focus on supporting the administration’s agenda,” says one CDC employee, who was granted anonymity out of concerns over retribution. “I worry that the next directors will not support and protect staff.”
President Donald Trump’s original pick to lead the CDC was David Weldon, a physician and previous Republican congressman from Florida who had a history of making statements questioning the safety of vaccines. But hours before his Senate confirmation hearing in March, the White House withdrew Weldon’s nomination. The administration then nominated Monarez.
The CDC leadership exits come amid recent vaccine policy upheaval by HHS secretary Robert F. Kennedy Jr., who in May removed the Covid-19 vaccine from the list CDC’s recommended vaccines for healthy children and pregnant women. The following month, he fired all 17 sitting members of the CDC’s Advisory Committee on Immunization Practices, a group of independent experts that makes science-based recommendations on vaccines.
In their place, he installed eight new members, including several longtime vaccine critics. “A clean sweep is necessary to reestablish public confidence in vaccine science,” Kennedy said in a statement at the time.
Earlier this month under Kennedy’s leadership, HHS canceled a half billion dollars in funding for research on mRNA vaccines. This month HHS also announced the reinstatement of the Task Force on Safer Childhood Vaccines, a federal advisory panel created by Congress in 1986 to improve vaccine safety and oversight for children in the US. The panel was disbanded in 1998, when it issued its final report. Public health experts worry that the panel is a move to further undermine established vaccine science.
Tech
Real-time technique directly images material failure in 3D to improve nuclear reactor safety and longevity

MIT researchers have developed a technique that enables real-time, 3D monitoring of corrosion, cracking, and other material failure processes inside a nuclear reactor environment.
This could allow engineers and scientists to design safer nuclear reactors that also deliver higher performance for applications like electricity generation and naval vessel propulsion.
During their experiments, the researchers utilized extremely powerful X-rays to mimic the behavior of neutrons interacting with a material inside a nuclear reactor.
They found that adding a buffer layer of silicon dioxide between the material and its substrate, and keeping the material under the X-ray beam for a longer period of time, improves the stability of the sample. This allows for real-time monitoring of material failure processes.
By reconstructing 3D image data on the structure of a material as it fails, researchers could design more resilient materials that can better withstand the stress caused by irradiation inside a nuclear reactor.
“If we can improve materials for a nuclear reactor, it means we can extend the life of that reactor. It also means the materials will take longer to fail, so we can get more use out of a nuclear reactor than we do now. The technique we’ve demonstrated here allows to push the boundary in understanding how materials fail in real-time,” says Ericmoore Jossou, who has shared appointments in the Department of Nuclear Science and Engineering (NSE), where he is the John Clark Hardwick Professor, and the Department of Electrical Engineering and Computer Science (EECS), and the MIT Schwarzman College of Computing.
Jossou, senior author of a study on this technique, is joined on the paper by lead author David Simonne, an NSE postdoc; Riley Hultquist, a graduate student in NSE; Jiangtao Zhao, of the European Synchrotron; and Andrea Resta, of Synchrotron SOLEIL. The research is published in the journal Scripta Materiala.
“Only with this technique can we measure strain with a nanoscale resolution during corrosion processes. Our goal is to bring such novel ideas to the nuclear science community while using synchrotrons both as an X-ray probe and radiation source,” adds Simonne.
Real-time imaging
Studying real-time failure of materials used in advanced nuclear reactors has long been a goal of Jossou’s research group.
Usually, researchers can only learn about such material failures after the fact, by removing the material from its environment and imaging it with a high-resolution instrument.
“We are interested in watching the process as it happens. If we can do that, we can follow the material from beginning to end and see when and how it fails. That helps us understand a material much better,” he says.
They simulate the process by firing an extremely focused X-ray beam at a sample to mimic the environment inside a nuclear reactor. The researchers must use a special type of high-intensity X-ray, which is only found in a handful of experimental facilities worldwide.
For these experiments they studied nickel, a material incorporated into alloys that are commonly used in advanced nuclear reactors. But before they could start the X-ray equipment, they had to prepare a sample.
To do this, the researchers used a process called solid state dewetting, which involves putting a thin film of the material onto a substrate and heating it to an extremely high temperature in a furnace until it transforms into single crystals.
“We thought making the samples was going to be a walk in the park, but it wasn’t,” Jossou says.
As the nickel heated up, it interacted with the silicon substrate and formed a new chemical compound, essentially derailing the entire experiment. After much trial-and-error, the researchers found that adding a thin layer of silicon dioxide between the nickel and substrate prevented this reaction.
But when crystals formed on top of the buffer layer, they were highly strained. This means the individual atoms had moved slightly to new positions, causing distortions in the crystal structure.
Phase retrieval algorithms can typically recover the 3D size and shape of a crystal in real-time, but if there is too much strain in the material, the algorithms will fail.
However, the team was surprised to find that keeping the X-ray beam trained on the sample for a longer period of time caused the strain to slowly relax, due to the silicon buffer layer. After a few extra minutes of X-rays, the sample was stable enough that they could utilize phase retrieval algorithms to accurately recover the 3D shape and size of the crystal.
“No one had been able to do that before. Now that we can make this crystal, we can image electrochemical processes like corrosion in real time, watching the crystal fail in 3D under conditions that are very similar to inside a nuclear reactor. This has far-reaching impacts,” he says.
They experimented with a different substrate, such as niobium doped strontium titanate, and found that only a silicon dioxide buffered silicon wafer created this unique effect.
An unexpected result
As they fine-tuned the experiment, the researchers discovered something else.
They could also use the X-ray beam to precisely control the amount of strain in the material, which could have implications for the development of microelectronics.
In the microelectronics community, engineers often introduce strain to deform a material’s crystal structure in a way that boosts its electrical or optical properties.
“With our technique, engineers can use X-rays to tune the strain in microelectronics while they are manufacturing them. While this was not our goal with these experiments, it is like getting two results for the price of one,” he adds.
In the future, the researchers want to apply this technique to more complex materials like steel and other metal alloys used in nuclear reactors and aerospace applications. They also want to see how changing the thickness of the silicon dioxide buffer layer impacts their ability to control the strain in a crystal sample.
“This discovery is significant for two reasons. First, it provides fundamental insight into how nanoscale materials respond to radiation—a question of growing importance for energy technologies, microelectronics, and quantum materials. Second, it highlights the critical role of the substrate in strain relaxation, showing that the supporting surface can determine whether particles retain or release strain when exposed to focused X-ray beams,” says Edwin Fohtung, an associate professor at the Rensselaer Polytechnic Institute, who was not involved with this work.
More information:
David Simonne et al, X-ray irradiation induced strain relaxation of dewetted Ni particles on modified Si substrate, Scripta Materialia (2025). DOI: 10.1016/j.scriptamat.2025.116940
This story is republished courtesy of MIT News (web.mit.edu/newsoffice/), a popular site that covers news about MIT research, innovation and teaching.
Citation:
Real-time technique directly images material failure in 3D to improve nuclear reactor safety and longevity (2025, August 27)
retrieved 27 August 2025
from https://techxplore.com/news/2025-08-real-technique-images-material-failure.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.
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