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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Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
TL;DR: This work proposes an approach to cross-domain semantic segmentation with the auxiliary geometric information, which can also be easily obtained from virtual environments, and achieves a clear performance gain compared to the baselines and various competing methods.
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Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation
TL;DR: The proposed SceneNet model is able to perform region-aware depth estimation by enforcing semantics consistency between stereo pairs and produces favorable results against the state-of-the-art approaches do.
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Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
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TL;DR: The proposed approach dynamically sets different confidence thresholds according to the prediction variance, rectifies the learning from noisy pseudo labels, and achieves significant improvements over the conventional pseudo label learning and yields competitive performance on all three benchmarks.
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End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks:
TL;DR: Competitive performance of the proposed ESP-VO to the state-of-the-art methods is shown, demonstrating a promising potential of the deep learning technique for VO and verifying that it can be a viable complement to current VO systems.
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Self-Supervised Learning With Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera
TL;DR: GLNet as mentioned in this paper proposes a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video, addressing the difficulty of acquiring realistic groundtruth for such tasks.
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