Omkar2118/datasets huggingface dataset:
SketchyVR
Full name: Sparse 3D VR Sketches

We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs for 1,005 chair shapes with large shapes diversity from the ShapeNetCore dataset from 50 participants. 

The collected sketches are provided as both obj files and point cloud files, along with corresponding time information in timestamp.txt. Each sketch obj file contains a collection of strokes, where each stroke consists of several edges.

This dataset is targeted for fine-grained 3D VR sketch to 3D shape retrieval in this paper. But it can also enable other novel applications, especially those that require a fine-grained angle such as fine-grained 3D shape reconstruction.

Introducing paper: Fine-Grained VR Sketching: Dataset and Insights
KArSL
Full name: Arabic Sign Language Dataset

KArSL (**K**FUPM **Ar**abic **S**ign **L**anguage) is an Arabic sign language (ArSL) database collected using Microsoft Kinect V2. The database consists of **502 sign words** constituting the sign words of eleven chapters of ArSL dictionary (Letters, Numbers, Health, Common verbs, Family, Characteristics, Directions and places, Social relationships, In house, Religion, and Jobs and professions). Each sign of the database is performed by three professional signers. The signers involved in this database are all male and their age is between 30 and 40 years. Each signer repeated **each sign 50 times** which resulted in a total of **75,300 samples** of the whole database **(502 x 3 x 50)** as shown in the table below.

Introducing paper: KArSL: Arabic Sign Language Database
GasHisSDB

Four pathologists from Longhua Hospital Shanghai University of Traditional Chinese Medicine provide 600 images of gastric cancer pathology images at size 2048$\times$2048 pixels. These images were scanned using a NewUsbCamera and digitized at $\times$20 magnification, tissue-level labels were also given by the four experienced pathologists. Based on that, five biomedical researchers from Northeastern University cropped them to 245,196 sub-sized gastric cancer pathology images, and two experienced pathologists from Liaoning Cancer Hospital and Institute perform the calibration. The 245,196 images were split to three sizes (160$\times$160, 120$\times$120, 80$\times$80) for two categories: abnormal and normal.

Introducing paper: GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer