TransCG is the first large-scale real-world dataset for transparent object depth completion and grasping, which contains 57,715 RGB-D images of 51 transparent objects and many opaque objects captured from different perspectives (~240 viewpoints) of 130 scenes under real-world settings. The samples are captured by two different types of cameras (Realsense D435 & L515). The following data is provided: - The 3D model of the transparent object; - The 6dpose of the transparent object in each viewpoint of each scene; - The raw RGB-D image, and the ground-truth refined depth image; - The mask of the transparent objects; - The ground-truth surface normals of every sample.
SuperCaustics is a simulation tool made in Unreal Engine for generating massive computer vision datasets that include transparent objects. SuperCaustics is a real-time, open-source simulation of transparent objects designed for deep learning applications. SuperCaustics features extensive modules for stochastic environment creation; uses hardware ray-tracing to support caustics, dispersion, and refraction; and enables generating massive datasets with multi-modal, pixel-perfect ground truth annotations. <p align="center"> <img src="https://github.com/MMehdiMousavi/SuperCaustics/raw/main/Assets/SuperCaustics.gif" alt="drawing" width="600"/> </p>
The dataset contains procedurally generated images of transparent vessels containing liquid and objects . The data for each image includes segmentation maps, 3d depth maps, and normal maps of of the liquid or object inside the transparent vessel, and the vessel. In addition, the properties of the materials inside the containers are given(color/transparency/roughness/metalness). In addition, a natural image benchmark for the 3d/depth estimation of objects inside transparent containers is supplied. 3d models of the objects (GTLF) are also supplied.