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Silver: Novel Rendering Engine for Data Hungry Computer Vision Models

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

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Silver: Novel Rendering Engine for Data Hungry Computer Vision Models. / Kerim, Abdulrahman; Soriano Marcolino, Leandro; Jiang, Richard.
2021. Paper presented at 2nd International Workshop on Data Quality Assessment for Machine Learning.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Kerim, A, Soriano Marcolino, L & Jiang, R 2021, 'Silver: Novel Rendering Engine for Data Hungry Computer Vision Models', Paper presented at 2nd International Workshop on Data Quality Assessment for Machine Learning, 15/08/21. <http://data-readiness-kdd-2021.mybluemix.net/#accepted-papers>

APA

Kerim, A., Soriano Marcolino, L., & Jiang, R. (2021). Silver: Novel Rendering Engine for Data Hungry Computer Vision Models. Paper presented at 2nd International Workshop on Data Quality Assessment for Machine Learning. http://data-readiness-kdd-2021.mybluemix.net/#accepted-papers

Vancouver

Kerim A, Soriano Marcolino L, Jiang R. Silver: Novel Rendering Engine for Data Hungry Computer Vision Models. 2021. Paper presented at 2nd International Workshop on Data Quality Assessment for Machine Learning.

Author

Kerim, Abdulrahman ; Soriano Marcolino, Leandro ; Jiang, Richard. / Silver: Novel Rendering Engine for Data Hungry Computer Vision Models. Paper presented at 2nd International Workshop on Data Quality Assessment for Machine Learning.

Bibtex

@conference{a7a3472d939647329ac12501339babd7,
title = "Silver: Novel Rendering Engine for Data Hungry Computer Vision Models",
abstract = "Large-scale synthetic data is needed to support the deep learning big-bang that started in the recent decade and influenced almost all scientific fields. Most of the synthetic data generation solutions are task-specific or unscalable while the others are expensive, based on commercial games, or unreliable. In this work, a new rendering engine called Silver is presented in detail. Photo-realism, diversity, scalability, and full 3D virtual world generation at run-time are the key aspects of this work. The photo-realism was approached by utilizing the state-of-the-art High Definition Render Pipeline (HDRP) of the Unity game engine. In parallel, the Procedural Content Generation (PCG) concept was employed to create a full 3D virtual world at run-time, while the scalability of the system was attained by taking advantage of the modular approach followed as we built the system from scratch. Silver can be used to provide clean, unbiased, and large-scale training and testing data for various computer vision tasks.",
keywords = "Synthetic data, Computer vision, Computer graphics",
author = "Abdulrahman Kerim and {Soriano Marcolino}, Leandro and Richard Jiang",
year = "2021",
month = aug,
day = "15",
language = "English",
note = "2nd International Workshop on Data Quality Assessment for Machine Learning : @ Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) ; Conference date: 15-08-2021",
url = "http://data-readiness-kdd-2021.mybluemix.net/",

}

RIS

TY - CONF

T1 - Silver: Novel Rendering Engine for Data Hungry Computer Vision Models

AU - Kerim, Abdulrahman

AU - Soriano Marcolino, Leandro

AU - Jiang, Richard

PY - 2021/8/15

Y1 - 2021/8/15

N2 - Large-scale synthetic data is needed to support the deep learning big-bang that started in the recent decade and influenced almost all scientific fields. Most of the synthetic data generation solutions are task-specific or unscalable while the others are expensive, based on commercial games, or unreliable. In this work, a new rendering engine called Silver is presented in detail. Photo-realism, diversity, scalability, and full 3D virtual world generation at run-time are the key aspects of this work. The photo-realism was approached by utilizing the state-of-the-art High Definition Render Pipeline (HDRP) of the Unity game engine. In parallel, the Procedural Content Generation (PCG) concept was employed to create a full 3D virtual world at run-time, while the scalability of the system was attained by taking advantage of the modular approach followed as we built the system from scratch. Silver can be used to provide clean, unbiased, and large-scale training and testing data for various computer vision tasks.

AB - Large-scale synthetic data is needed to support the deep learning big-bang that started in the recent decade and influenced almost all scientific fields. Most of the synthetic data generation solutions are task-specific or unscalable while the others are expensive, based on commercial games, or unreliable. In this work, a new rendering engine called Silver is presented in detail. Photo-realism, diversity, scalability, and full 3D virtual world generation at run-time are the key aspects of this work. The photo-realism was approached by utilizing the state-of-the-art High Definition Render Pipeline (HDRP) of the Unity game engine. In parallel, the Procedural Content Generation (PCG) concept was employed to create a full 3D virtual world at run-time, while the scalability of the system was attained by taking advantage of the modular approach followed as we built the system from scratch. Silver can be used to provide clean, unbiased, and large-scale training and testing data for various computer vision tasks.

KW - Synthetic data

KW - Computer vision

KW - Computer graphics

M3 - Conference paper

T2 - 2nd International Workshop on Data Quality Assessment for Machine Learning

Y2 - 15 August 2021

ER -