The Cityscapes Dataset. We present a new large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames.

The Cityscapes Dataset focuses on semantic understanding of urban street scenes. In the following, we give an overview on the design choices that were made to target the dataset’s focus. Features

The videos below provide further examples of the Cityscapes Dataset. The first video contains roughly 1000 images with high quality annotations overlayed. The second video visualizes the precomputed depth maps using the corresponding right stereo views.

dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic la-beling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different

2020年8月30日 · Cityscapes 3D is an extension of the original Cityscapes with 3D bounding box annotations for all types of vehicles as well as a benchmark for the 3D detection task. For more details please refer to our paper, presented at the CVPR 2020 Workshop on Scalability in Autonomous Driving.

Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and …

Comprehensive empirical evaluations on the challenging Cityscapes, Synthia, SUN RGB-D, ScanNet and Freiburg Forest datasets demonstrate that our architecture achieves state-of-the-art performance while simultaneously being efficient in terms of …

Cityscapes 3D Dataset Released August 30, 2020; Coming Soon: Cityscapes 3D June 16, 2020; Robust Vision Challenge 2020 June 4, 2020; Panoptic Segmentation May 12, 2019; Search Website. Search for: Contact. Cityscapes Team. Imprint / Impressum. Data Protection / Datenschutzhinweis

large-scale dataset for (1) assessing the performance of vision al- gorithms for different tasks of semantic urban scene understand- ing, including scene labeling, instance-level scene labeling, and

If you use this dataset in your research, please cite this publication: M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The Cityscapes Dataset for Semantic Urban Scene Understanding,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016.