Omkar2118/datasets huggingface dataset:
semi-indoor

collected by one VLP-16 in a small vehicle (1m x 1m)

Introducing paper: A Dynamic Points Removal Benchmark in Point Cloud Maps
Super-CLEVR-3D
Full name: Super-CLEVR-3D

**Super-CLEVR-3D** is a visual question answering (VQA) dataset where the questions are about the explicit 3D configuration of the objects from images (i.e. **3D poses**, **parts**, and **occlusion**). It consists of objects from 5 categories: aeroplanes, buses, bicycles, cars and motorbikes. The rendered objects are from [CGParts](https://github.com/qliu24/render-3d-segmentation) dataset, with the same setting as [Super-CLEVR](https://arxiv.org/abs/2212.00259) dataset.

Introducing paper: 3D-Aware Visual Question Answering about Parts, Poses and Occlusions
WorldFloods

WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations
AASCE
Full name: Accurate Automated Spinal Curvature Estimation

The purpose of this challenge is to  investigate (semi-)automatic spinal curvature estimation algorithms. Participant will have to submit results of Cobb angle for all the test data. 

## Evaluation:
Submissions are scored based on symmetric mean absolute percentage error (SMAPE).
WBM
Full name: Predicting stable crystalline compounds using chemical similarity

We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data-mining experimental databases. In this way we build 189,981 possible crystal phases, including 18,479 that are on the convex hull of stability. The resulting success rate of 9.72% is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials, and comparable with speed-up factors of machine learning filtering procedures. As a characterization of the set of 18,479 stable compounds, we calculate their electronic band gaps, magnetic moments, and hardness. Our approach, that can be used as a filter on top of any high-throughput scheme, enables us to efficiently extract stable compounds from tremendously large initial sets, without any initial assumption on their crystal structures or chemical compositions.