Geometric deep learning specialist Physna has raised $56 million in its latest round of venture capital funding.
Led by Tiger Global with contributions from GV and Sequoia Capital, the financing comes just five months after the company’s last Series B Round, which raised $20 million. With this round, Physna has taken its total funding to more than $86 million. The firm states that it will use the capital to further develop its deep learning-based geometric search engine, Thangs, which operates as a social platform for 3D printing models.
Alongside the funding news, Physna has also announced the launch of Instant AR, a major Thangs software update that enables users to convert the platform’s 3D models into AR formats. These AR objects can then be placed into real world environments using just a smartphone camera. Available today to all Thangs users, Instant AR is compatible with both Android and iOS native browsers, and doesn’t require any extra hardware or special apps.
“Physna makes it possible for computers to understand physical objects by turning 3D models into digital code,” said John Curtius, Partner at Tiger Global. “Its technology will help companies solve incredibly complex, previously unsolvable problems that have massive economic implications. Physna’s a pioneer and leader in this space, with a lot of opportunity in front of it.”
Disrupting search algorithms with Physna
Founded in 2016, Physna set out with the goal of teaching computers to think in 3D. The company’s proprietary algorithms are based on geometric deep learning technology, and they work by codifying 3D models into data that is usable by conventional software applications.
As such, Physna’s technology is capable of analyzing, comparing, and searching for 3D models based on just their geometries. Leveraging the company’s enterprise platform, an engineering professional can find a new 3D model for an assembly just by searching with predefined 3D objects, partial models, or geometric measurements.
With the collaborative functionality of the platform, stakeholders throughout an organization can also search for objects via images and 3D scans. For example, Physna would enable a procurement manager to see where a specific part is being used throughout a company, and in what quantities.
“As the physical and digital worlds continue to merge and computing interfaces evolve, the ability to standardize data and search across the 3D digital world will become more critical,” said Brian Bendett, Partner at GV. “I’m excited to work with the Physna team as they continue building a new standard for 3D search.”
Thangs: a social platform for 3D models
As advanced as the enterprise platform is, Physna attributes much of its growth to its free-to-use Thangs website. The social platform was launched back in 2020, and already has more than 250,000 members. According to Physna, Thangs is the world’s most powerful geometric search engine, hosting over 2.5 million 3D models – many of which can be downloaded and 3D printed.
With Thangs, users can search based on an object’s physical properties and measurements, and receive predictions about its function, cost, materials, and performance. The site also allows its users to upload, share, and collaborate on 3D models with ease. Much like GitHub, the site has automated version control, and even enables users to like and comment on models to save them for later. Since accounts can make their work accessible on their profiles, Thangs can also act as a portfolio or resume for design professionals.
“Thangs is an integral piece of Physna’s growth and future,” said Physna CEO Paul Powers. “As we work toward our mission of indexing the physical world, Thangs will be our connection to the millions of professional and hobbyist 3D engineers and designers who will help grow and develop spatial computing alongside us.”
Machine learning in additive manufacturing
With ongoing advancements in machine learning, it’s no surprise that the technology is making its way into additive manufacturing. A team of researchers from Argonne National Laboratory and Texas A&M University have previously developed a machine learning-based approach to defect detection in 3D printed parts. Using real-time temperature data, the scientists were able to make correlative links between thermal history and the formation of defects during laser powder bed fusion.
Elsewhere, Markforged, a U.S. manufacturer of metal and composite 3D printers, recently launched its AI-based Blacksmith software for use with the X7 3D printer. Leveraging the X7’s own existing laser micrometer and a patented scanning algorithm, Blacksmith accurately measures the dimensional precision of parts as they are being printed.
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Featured image shows Physna’s software in the workplace. Photo via Physna.
