Silver: Novel Rendering Engine for Data Hungry Computer Vision Tasks

To Watch the Full Video: (https://youtu.be/Ktlx5bgJLXE)

Abstract

In this work, a new rendering engine called Silver is presented in details. The full-system code was released with a sample dataset. The dataset is just meant to show the capability of the system and researchers are welcomed to generate their own datasets.

System Requirements

To run the system smoothly on your device, the following are suggested:

• Ubuntu 18.04.5 LTS.
• Unity HDRP (Unity version 2020.1.17f1).
• GeForce GTX 1080 Ti
• Vulkan API

System Features

Ground-truths

Our system automatically provides ground-truth for depth estimation, normal map, semantic segmentation, instance segmentation and body pose estimation.Image below shows one RGB frame with its corresponding ground-truths for the mentioned computer vision tasks. In addition to other textual information describing the time of the day, the weather condition, and many other useful information.

Photo-realism

HDRP is based on the Scriptable Rendering Pipeline (SRP). Generally, it is intended for high visual fidelity applications. The other key feature of HDRP is the Physical Light Unit (PLU) that relies on real-life lighting measurable values. All of these attributes together contribute to the final photo-realistic rendering shown below.

Diversity

To avoid the over-fitting to the visual features of the synthetic world, the content of the environment is diversified by including a wide set of 3D models, textures, animations, weather conditions, illumination and lighting, and recording set-ups. Examples frames from the supported weather conditions and time of days are shown below.

Code and Dataset

• The complete project can be downloaded using this link: Click
• The sample dataset can be downloaded using this link: Click

Sample Videos

To see a long video, please click on the images below.

Contact Authors

• Abdulrahman Kerim, PhD Student, at Lancaster University, a.kerim@lancaster.ac.uk
• Leandro Soriano Marcolino, Lecturer at Lancaster University, l.marcolino@lancaster.ac.uk

Licence

• The dataset and the framework are made freely available to academic and non-commercial purposes. They are provided “AS IS” without any warranty.
• If you use the dataset or the framework feel free to cite our work.

Citation

• Our Paper got accepted to 2nd International Workshop on Data Quality Assessment for Machine Learning @ SIGKDD.

@inproceedings{kerim2021silver,
  title={Silver: Novel Rendering Engine for Data Hungry Computer Vision Models},
  author={Kerim, Abdulrahman and Soriano Marcolino, Leandro and Jiang, Richard},
  booktitle={2nd International Workshop on Data Quality Assessment for Machine Learning},
  year={2021}
}