Omni ↦ Data (Under Construction)

A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans

Annotator Repo · >> [Starter Data] << · Tooling + Training Repo · Reference Code · Project Website

---

Omnidata Starter Dataset

This repository contains the Starter Dataset generated by the Omnidata Annotator from our paper:

Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV2021)

Table of Contents

• Introduction
• Downloading the Full Starter Dataset
• Sample Data
• Data Statistics
• Citing

Introduction:

We provide a Starter Dataset generated by Omnidata Pipeline from some existing 3D datasets. It contains more than 14 million images from over 2000 spaces with 21 different mid-level vision cues per image. The dataset covers very diverse scenes (indoors and outdoors) and views (scene- and object-centric).

Downloading the Full Dataset:

The full starter dataset will be available to download soon.

Sample Data:

We provide a sample data from a random building in our GSO + Replica dataset split, which is created by scattering Google Scanned Objects around Replica buildings using the Habitat environment. This is only a sample scene (with mostly object-centric views) from over 2000 scenes available in the full dataset.

You can download and untar the sample data with the following command:

wget https://drive.switch.ch/index.php/s/MkygxW0WLiLKsNz/download
tar -xf download

Now the sample dataset is available in the folder omnidata_sample_dataset.

Sample Data (GSO+Replica)

Data Statistics

	Taskonomy	Replica	GSO+Replica	HM3D
Field of View
Camera Pitch
Camera Roll
Obliqueness Angle
Camera Distance
Views per Point

Citation

If you find this dataset useful in your research, please cite our paper:

@inproceedings{eftekhar2021omnidata,
  title={Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets From 3D Scans},
  author={Eftekhar, Ainaz and Sax, Alexander and Malik, Jitendra and Zamir, Amir},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={10786--10796},
  year={2021}
}