Physna, a midwestern U.S. startup founded in 2015, is trying to do for physical object (3D model) search what Google did for text and image search. Using geometric deep-learning technology and proprietary algorithms, Physna is able to understand, map and compare 3D models and index them based on their geometry. While it has been possible to search for 3D models using text, images, tags and more, this is the first time that searching for physical objects based on their fundamental geometry, their physical ‘DNA’ (hence the name PHYSNA according to its founder Paul Powers), has been made possible and available, with the launch of Thangs.com.
“We live in a 3D world, but digital technology is two-dimensional,” said Paul Powers, CEO of Physna. “Over 70% of the economy is centered around physical goods, but less than 1% of software is capable of handling 3D data. Physna was founded on the principle that computers should be taught to “think” in 3D, and accurately describe the real, 3D world around us. By enabling 3D models to be treated and analyzed like other code, Physna’s technology bridges the gap between the physical world and digital world of software. By democratizing the ability to design, interact with and analyze 3D models of the world around them, more people will have the ability to create and drive innovation in product design, 3D printing, augmented and virtual reality, gaming, healthcare and beyond.”
By identifying specific geometry ‘clusters’, the proprietary algorithms characterize and categorize 3D models in a unique way – and directly use this to search for other models that may be similar, different, or exact matches. With this approach of decomposing and linking 3D models by their geometry, Physna is able to capture 10,000 times more data points than a traditional scanned model, by codifying 3D model data for use in software applications. It essentially provides a platform for 3D designers and engineers similar to what software engineers have.
Continue reading… “Physna aims to be Google of 3D search with geometry-based AI”