FlyingFrog/LAST_Large_Shapes_And_Textures_Dataset

LAS&T Large Shapes And Textures Dataset

LAS&T is a large scale highly 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/train models on identifying/retrieval of any shape, texture, and material in any setting and environment, without being limited to specific types or classes of objects/shapes/materials/textures, 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 material and texture, orientation, size, illuminations and environments. 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, a real-world images benchmark for 3D shapes recognition is also supplied.

Main Dataset webpage

Paper: Shape and Texture Recognition in Large Vision-Language Models

LAS&T Project Website

Dataset Structure

The dataset contain four parts parts:

3D shape recognition and retrieval.

2D shape recognition and retrieval.

3D Materials recognition and retrieval.

2D Texture recognition and retrieval.

3D shape recognition real-world natural images benchmark

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 (SHAPES_COLLECTION_350k.zip)

The scripts used to generate and test the dataset are supplied as in SCRIPT** files.

For 2d/3d material/texuture recognition and retrieval: The goal is to identify the same material or textures in different images where the material/texture appear on different shapes/objects enviroments and light conditions. For 2d/3d shapes recognition and retrieval: The goal is to identify the same shape in different images with different shape materials, orientations, environments, and lighting.

Files

Real_Images_3D_shape_matching_Benchmarks.zip contains real-world image benchmarks for 3D shapes.

Files containing the word 'GENERAL_LARGE_SET' contains synthetic images that can be used for training or testing, the type of data (2D shapes, 3D shapes, 2D textures, 3D materials) that appears in the file name, as well as the number of images. Files containing MultiTests contain a number of different tests in which only a single aspect of the aspect of the instance is changed (for example only the background.) File containing "SCRIPTS" contain data generation testing scripts. Images containing "examples" are example of each test. Shapes Collections

Data structure

For 2D and 3D shapes: All jpg images that are in the exact same subfolder contain the exact same shape (but with different texture/color/background/orientation). For 2D textures and 3D materials: All jpg images that are in the exact same subfolder contain the exact same material (but with different objects/shapes/background/orientation).

Real-world image data:

For 3D shape recognition and retrieval, we also supply a real-world natural image benchmark. With a variety of natural images containing the exact same 3D shape but made/coated with different materials and in different environments and orientations. The goal is again to identify the same shape in different images.

The benchmark is available at: Real_Images_3D_shape_matching_Benchmarks.zip

Evaluating and Testing

For evaluating and testing see: SCRIPTS_Testing_LVLM_ON_LAST_VQA.zip This can be use to test leading LVLMs using api, create human tests, and in general turn the dataset into multichoice question images similar to the one in the paper.

Textures and PBR Materials Assets Download

100K 2D textures images used in generating the dataset available from the Vastexture dataset

300K PBR materials use in generating the dataset available from the Vastexture dataset