OmniFlow
News
- 2021-04-16 - OmniFlow (v1.0) dataset released
Abstract
Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end, large training datasets are required to improve the accuracy of optical flow estimation. Our paper presents OmniFlow: a new synthetic omnidirectional human optical flow dataset. Based on a rendering engine we create a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during the data capturing process. The simulation has as output rendered images of household activities and the corresponding forward and backward optical flow. To verify the data for training volumetric correspondence networks for optical flow estimation we train different subsets of the data and test on OmniFlow with and without Test-Time-Augmentation. As a result we have generated 23,653 image pairs and corresponding forward and backward optical flow.
Paper
BibTeX
If you use the data set in your work, please don't forget to cite:
@inproceedings{Seidel_2021_CVPRW,
author = {Roman Seidel and André Apitzsch and Gangolf Hirtz},
title = {OmniFlow: Human Omnidirectional Optical Flow},
booktitle = {The Second OmniCV Workshop: Omnidirectional Computer Vision in Research and Industry, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}
License
This data set is licensed under a Creative Commons Attribution 4.0 International License.