WorldGen: A Large Scale Generative Simulator
Chahat Deep Singh, Riya Kumari, Cornelia Ferm\"uller, Nitin J. Sanket,, Yiannis Aloimonos

TL;DR
WorldGen is an open-source framework that autonomously generates large-scale, photorealistic 3D scenes with detailed annotations, aiming to improve data quality and diversity for training deep learning models in computer vision.
Contribution
It introduces a flexible, controllable generative simulator for creating diverse, photorealistic datasets with ground truth annotations, addressing limitations of existing synthetic data methods.
Findings
Effective in generating diverse, photorealistic scenes
Improves deep optical flow performance
Reduces manual data collection effort
Abstract
In the era of deep learning, data is the critical determining factor in the performance of neural network models. Generating large datasets suffers from various difficulties such as scalability, cost efficiency and photorealism. To avoid expensive and strenuous dataset collection and annotations, researchers have inclined towards computer-generated datasets. Although, a lack of photorealism and a limited amount of computer-aided data, has bounded the accuracy of network predictions. To this end, we present WorldGen -- an open source framework to autonomously generate countless structured and unstructured 3D photorealistic scenes such as city view, object collection, and object fragmentation along with its rich ground truth annotation data. WorldGen being a generative model gives the user full access and control to features such as texture, object structure, motion, camera and lens…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · Advanced Vision and Imaging · Generative Adversarial Networks and Image Synthesis
