VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments

• URL: http://arxiv.org/abs/2308.09632v1
• Date: Fri, 18 Aug 2023 15:44:45 GMT
• Title: VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments
• Authors: Oliver Grau and Korbinian Hagn
• Abstract summary: The VALERIE tool pipeline is a synthetic data generator developed to contribute to the understanding of domain-specific factors. The VALERIE22 dataset was generated with the VALERIE procedural tools pipeline providing a photorealistic sensor simulation. The dataset provides a uniquely rich set of metadata, allowing extraction of specific scene and semantic features.
• Score: 5.439020425819001
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The VALERIE tool pipeline is a synthetic data generator developed with the goal to contribute to the understanding of domain-specific factors that influence perception performance of DNNs (deep neural networks). This work was carried out under the German research project KI Absicherung in order to develop a methodology for the validation of DNNs in the context of pedestrian detection in urban environments for automated driving. The VALERIE22 dataset was generated with the VALERIE procedural tools pipeline providing a photorealistic sensor simulation rendered from automatically synthesized scenes. The dataset provides a uniquely rich set of metadata, allowing extraction of specific scene and semantic features (like pixel-accurate occlusion rates, positions in the scene and distance + angle to the camera). This enables a multitude of possible tests on the data and we hope to stimulate research on understanding performance of DNNs. Based on performance metric a comparison with several other publicly available datasets is provided, demonstrating that VALERIE22 is one of best performing synthetic datasets currently available in the open domain.

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