3D Shape Recognition

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. Hypersim is a photorealistic synthetic dataset for holistic indoor scene understanding. It contains 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Source: https://github.com/apple/ml-hypersim Image Source: https://github.com/apple/ml-hypersim

Large Shape and Texture dataset (LAS&T) is a giant dataset of shapes and textures for tasks of visual shapes and textures identification and retrieval from single image. LAS&T is the largest and most diverse dataset for shape, texture and material recognition and retrieval in 2D and 3D with 650,000 images, based on real world shapes and textures ## Overview The LAS&T Dataset aims to test the most basic aspect of vision in the most general way. Mainly the ability to identify any shape, texture, and material in any setting and environment, without being limited to specific types or classes of objects, materials, and environments. For shapes, this means identifying and retrieving any shape in 2D or 3D with every element of the shape changed between images, including the shape material and texture, orientation, size, and environment. For textures and materials, the goal is to recognize the same texture or material when appearing on different objects, environments, and light conditions. The dataset relies on shapes, textures, and materials extracted from real-world images, leading to an almost unlimited quantity and diversity of real-world natural patterns. Each section of the dataset (shapes, and textures), contains 3D parts that rely on physics-based scenes with realistic light materials and object simulation and abstract 2D parts. In addition, the real-world benchmark for 3D shapes. ## The dataset divided to several parts 3D shape recognition and retrieval. 2D shape recognition and retrieval. 3D Materials recognition and retrieval. 2D Texture recognition and retrieval. Each can be used independently for training and testing. Additional assets are a set of 350,000 natural 2D shapes extracted from real-world images 3D shape recognition real-world images benchmark