The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
- pp 3213-3223
TLDR
This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.Abstract:
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current 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 labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.read more
Citations
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CCNet: Criss-Cross Attention for Semantic Segmentation
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1 year, 1000 km: The Oxford RobotCar dataset:
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Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang,Ke Sun,Tianheng Cheng,Borui Jiang,Chaorui Deng,Yang Zhao,Dong Liu,Yadong Mu,Mingkui Tan,Xinggang Wang,Wenyu Liu,Bin Xiao +11 more
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Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
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Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
TL;DR: This work proposes a unsupervised framework to learn a deep convolutional neural network for single view depth prediction, without requiring a pre-training stage or annotated ground-truth depths, and shows that this network trained on less than half of the KITTI dataset gives comparable performance to that of the state-of-the-art supervised methods for singleView depth estimation.
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