Pipeline for Text-to-Image Panoramic Road Scene Generation and Evaluation

The Panoramic Road Scene Generation (PRSG) pipeline is a framework designed to generate realistic and contextually accurate panoramic road scenes from textual descriptions. This approach leverages a combination of a stable diffusion model and super-resolution techniques to produce panoramic images for autonomous vehicle (AV) simulations and VR/AR applications. By generating detailed panoramic road scenes viewable in a VR HMD, an additional avenue for easily creating and simulating vehicular scenarios is possible. The pipeline utilizes a customized dataset with specifically tailored captions to fine-tune the generation process, ensuring both visual fidelity and contextual relevance. The evaluation framework includes both subjective and objective metrics to assess to the quality and applicability of the generated images. A pilot study determined the generated road scenes to be relatively realistic and acceptable for viewing. Potential applications include the creation of new synthetic scenes for simulating AV scenarios, or training and validation of autonomous systems.

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Publication Details

Published
2024-09-17
DOI
https://doi.org/10.1145/3641308.3685038
Primary Topic
Automated Road and Building Extraction
Type
article
Field-Weighted Citation Impact
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article

Pipeline for Text-to-Image Panoramic Road Scene Generation and Evaluation

Jeannie S.A. Lee, Pai Chet Ng, Kan Chen, Ryan Yan Hern Sim
Automated Road and Building Extraction
article

Pipeline for Text-to-Image Panoramic Road Scene Generation and Evaluation

Jeannie S.A. Lee, Pai Chet Ng, Kan Chen, Ryan Yan Hern Sim
article en

Abstract

The Panoramic Road Scene Generation (PRSG) pipeline is a framework designed to generate realistic and contextually accurate panoramic road scenes from textual descriptions. This approach leverages a combination of a stable diffusion model and super-resolution techniques to produce panoramic images for autonomous vehicle (AV) simulations and VR/AR applications. By generating detailed panoramic road scenes viewable in a VR HMD, an additional avenue for easily creating and simulating vehicular scenarios is possible. The pipeline utilizes a customized dataset with specifically tailored captions to fine-tune the generation process, ensuring both visual fidelity and contextual relevance. The evaluation framework includes both subjective and objective metrics to assess to the quality and applicability of the generated images. A pilot study determined the generated road scenes to be relatively realistic and acceptable for viewing. Potential applications include the creation of new synthetic scenes for simulating AV scenarios, or training and validation of autonomous systems.

Singapore Institute of Technology (SG), University of Glasgow (GB)
Sustainable cities and communities
Openalex Percentile: Top 80%
Automated Road and Building Extraction
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