· 2017
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
- 1,389citations
- 2017year
Short summary
A new, large-scale street-level image dataset, Mapillary Vistas, has been released, featuring 25,000 high-resolution images with dense, fine-grained annotations for 66 object categories and instance-specific labels for 37 classes.
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Key points
- Dataset contains 25,000 high-resolution street-level images.
- Features dense, fine-grained annotations for 66 object categories.
- Includes instance-specific labels for 37 classes.
- Dataset is 5x larger than Cityscapes in fine annotations.
- Images represent global diversity in capture conditions and devices.
AI-generated from the title and abstract; the full text is not read.
Abstract
The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. Our dataset is 5× larger than the total amount of fine annotations for Cityscapes and contains images from all around the world, captured at various conditions regarding weather, season and daytime. Images come from different imaging devices (mobile phones, tablets, action cameras, professional capturing rigs) and differently experienced photographers. In such a way, our dataset has been designed and compiled to cover diversity, richness of detail and geographic extent. As default benchmark tasks, we define semantic image segmentation and instance-specific image segmentation, aiming to significantly further the development of state-of-the-art methods for visual road-scene understanding.
The authors' abstract, as published at the source., 2017 · DOI ↗
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Field: Ocean Engineering
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