MagiScan
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MagiScan is a mobile 3D scanning application driven by AI that allows users to effortlessly generate high-quality 3D models of real-world items using their iOS or Android devices. Designed to be straightforward, efficient, and cost-effective, it caters to both professionals and casual users alike. Offering the ability to export models in multiple formats such as USDZ, GTLF, GLB, OBJ, STL, FBX, and PLY, the app is perfectly suited for eCommerce, game development, virtual reality, and additional fields. MagiScan stands out for its rapid digitization capabilities, effectively connecting the physical and virtual worlds, contributing to its top rankings on the App Store and Google Play across numerous countries.
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MathHandwriting is a tool driven by AI that transforms handwritten math equations into digital LaTeX code through an API, creating a link between analog and digital math. It is especially beneficial for students, educators, and researchers regularly handling intricate mathematical notation who wish to simplify the digitization of handwritten solutions for use in publications, presentations, or digital sharing. MathHandwriting allows users to skip the cumbersome and error-prone process of manually typing equations, thereby saving time and boosting productivity while maintaining precision in digital mathematical expressions.
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Ailiverse NeuCore is an API solution for computer vision that decreases the data requirements for training and speeds up GPU training. It offers functionalities for image classification, image segmentation/object detection, action recognition, deepfake detection, and OCR.
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Segment Anything AI (Meta) provides the Segment Anything Model (SAM), an AI tool capable of isolating any object within any image. SAM is promptable and exhibits zero-shot generalization to novel images and objects, utilizing a range of input prompts that allow seamless integration with other AI systems. It can also be trained to label images and enhance its dataset. The SAM model is crafted to be efficient and adaptable, optimizing its data engine's performance. Contributors to the project include Alexander Kirillov, Eric Mintun, Nikhila Ravi, among others. The code is accessible on GitHub, and users can subscribe to their newsletter for updates on their latest research advancements.