3D Object Retrieval

The ModelNet40 dataset contains synthetic object point clouds. As the most widely used benchmark for point cloud analysis, ModelNet40 is popular because of its various categories, clean shapes, well-constructed dataset, etc. The original ModelNet40 consists of 12,311 CAD-generated meshes in 40 categories (such as airplane, car, plant, lamp), of which 9,843 are used for training while the rest 2,468 are reserved for testing. The corresponding point cloud data points are uniformly sampled from the mesh surfaces, and then further preprocessed by moving to the origin and scaling into a unit sphere. Source: Geometric Feedback Network for Point Cloud Classification

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

The RBO dataset of articulated objects and interactions is a collection of 358 RGB-D video sequences (67:18 minutes) of humans manipulating 14 articulated objects under varying conditions (light, perspective, background, interaction). All sequences are annotated with ground truth of the poses of the rigid parts and the kinematic state of the articulated object (joint states) obtained with a motion capture system. We also provide complete kinematic models of these objects (kinematic structure and three-dimensional textured shape models). In 78 sequences the contact wrenches during the manipulation are also provided.

Dataset for testing the ability of Vision Language Models (LVM) to recognize and match 3D objects of the exact same 3D shapes but with different orientation/materials/textures/ environments and light conditions.