Press Coverage

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February 7, 2022

Wired

A New Trick Lets Artificial Intelligence See in 3D

THE CURRENT WAVE of artificial intelligence can be traced back to 2012, and an academic contest that measured how well algorithms could recognize objects in photographs. That year, researchers found that feeding thousands of images into an algorithm inspired loosely by the way neurons in a brain respond to input produced a huge leap in accuracy. The breakthrough sparked an explosion in academic research and commercial activity that is transforming some companies and industries. Now a new trick, which involves training the same kind of AI algorithm to turn 2D images into a rich 3D Full Story

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January 1, 2022

Communications of the ACM

Technical Perspective: Neural Radiance Fields Explode on the Scene

Imagine you could capture a 3D scene and later revisit that scene from different viewpoints, perhaps seeing the action as it unfolded at capture time. We are accustomed to snapping 2D photographs or videos, which are then compactly stored on our phones or in the cloud. In contrast, the corresponding process for 3D capture is quite cumbersome. Traditionally, it involves taking lots of images of the scene, applying photogrammetry techniques to reconstruct a dense surface reconstruction, and then cleaning up manually. Full Story

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June 20, 2019

Tech Crunch

Intel is doing the hard work necessary to make sure robots can operate your microwave

Training computers and robots to not only understand and recognize objects (like an oven, for instance, as distinct from a dishwasher) is pretty crucial to getting them to a point where they can manage the relatively simple tasks that humans do every day. But even once you have an artificial intelligence trained to the point where it can tell your fridge from your furnace, you also need to make sure it can operate the things if you want it to be truly functional. That's where new work from Intel AI researchers, working in collaboration with UCSD and Stanford, comes in -- Full Story

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June 19, 2019

The Robot Report

Intel, OSU, Stanford, and UC San Diego work on reinforcement learning, PartNet could help household robots

Intel Corp. has been a strong supporter of research into artificial intelligence, machine learning, and computer vision, and two of its collaborations have implications for robots that operate in dynamic environments such as households. This week, Intel AI Lab and researchers at Oregon State University and the University of California, San Diego, presented papers that offer a new model for reinforcement learning and a massive dataset for training object recognition, respectively. "We want to expand two approaches for machine learning to explore spaces with a more complex set of interactions," Full Story

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June 17, 2019

Intel AI

Introducing PartNet: The First Large-Scale Dataset with Fine-Grained, Hierarchical, Instance-Level Part Annotations

Identifying objects and their parts is critical to how humans understand and interact with the world. For example, using a stove requires not only identifying the stove itself, but also its subcomponents: burners, control knobs, etc. This same capability is essential to many AI vision, graphics, and robotics applications, including predicting object functionality, human-object interaction, simulation, shape editing, and shape generation. This wide range of applications has spurred great demand for large 3D datasets with part annotations. Full Story

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June 17, 2019

IEEE Spectrum

Massive 3D Dataset Helps Robots Understand What Things Are

One of the things that makes humans so great at adapting to the world around us is our ability to understand entire categories of things all at once, and then use that general understanding to make sense of specific things that we?ve never seen before. For example, consider something like a lamp. We?ve all seen some lamps. Nobody has seen every single lamp there is. But in most cases, we can walk into someone's house for the first time and easily identify all their lamps and how they work. Full Story

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June 17, 2019

Venture Beat

Intel highlights AI that can see around corners, coach children on the autism spectrum, and more

This week marks the start of the Conference on Computer Vision and Pattern Recognition (CVPR), an academic convention cosponsored by the Institute of Electrical and Electronic Engineers' Computer Society and the Computer Vision Foundation. It's grown substantially since 1983, its inaugural year, and now sees thousands of research paper from tens of thousands of researchers submitted annually. In fact, for the first time, this year it accepted over 1,000 studies from a pool of 5,165. Intel's research team is one of the many who put forth their work for consideration Full Story

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February 22, 2018

GIZMODO

Just Look at This Freakin' CGI Fur

When you look at fur, the light has taken an extended journey before hitting your eye, bouncing around lots of fur fibers and their microscopic structures. You can imagine that it's quite difficult for filmmakers to render these structures in CGI. Researchers at the Universities of California in San Diego and Berkeley have devised an improvement over fur rendering that they think is both more efficient and more practical. It's ten times faster than previous methods, and incorporates a more realistic model of fur to create more realistic images. Full Story

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February 22, 2018

New Atlas

Computer-generated fur gets more realistic

If you're not satisfied with the realism of computer-generated animals in movies and games, then you might be interested in the latest news out of the University of California San Diego. Working with colleagues from UC Berkeley, computer scientists there have developed a new method of rendering fur that's reportedly much more accurate than existing techniques. Currently, fur is simulated in the same manner as is human hair - the computer utilizes a model that follows a ray of light as it bounces from one fur fiber to another. The technique requires a lot of processing power, Full Story

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February 21, 2018

Electronics 360 powered by IEEE GlobalSpec

Watch: Rendering Realistic Fur

Scientists in California get to work on the coolest things. Take, for instance, the challenge of making computer-generated creatures look more realistic for their role in computer-animated films, special effects and video games. Recently, at the SIGGRAPH Asia conference in Thailand, researchers from UC San Diego and UC Berkeley presented their method for dramatically improving the way computers simulate fur - specifically, the way light bounces within an animal's pelt. Full Story

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