The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes

By Germán Ros, Laura Sellart, Joanna Materzynska, David Vázquez, Antonio M. López. 2016

2,458 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation (2016, background): “More recently, it has been shown that deep networks can also boost their performance when pretrained on data other than natural images, such as video games [26, 28] or clipart [3], and this an interesting direction to explore.”
Unsupervised Image-to-Image Translation Networks (2017, methodology): “mingyuliutw/unit. Synthetic to real. In Figure 3, we showed several example results achieved by applying the proposed framework to translate images between the synthetic images in the SYNTHIA dataset [23] and the real images in the Cityscape dataset [2]. For the real to synthetic translation, we fou”
Learning from Simulated and Unsupervised Images through Adversarial Training (2016, background): “various prediction tasks, including gaze estimation [43], text detection and classification in RGB images [9, 15], font recognition [42], object detection [10, 27], hand pose estimation in depth images [38, 37], scene recognition in RGB-D [11], semantic segmentation of urban scenes [31], and human p”
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding (2020, background): “No existing synthetic dataset or simulator addresses all of these limitations, including those that target outdoor scene understanding [8, 20, 23, 34, 38, 41, 55, 56, 58, 60, 65, 76, 77].”
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, methodology): “There are several popular synthetic datasets like SYNTHIA [44] and CARLA [13].”
Domain Adaptation for Traffic Density Estimation (2021, methodology): “A very appealing application of domain adaptation concerns synthetic data, which has led to the development of several synthetic datasets, such as ViPeD ((Amato et al., 2019), (Ciampi et al., 2020a)) for pedestrian detection and SYNTHIA (Ros et al., 2016) for semantic segmentation and autonomous dri”
Multimodal Unsupervised Image-to-Image Translation (2018, methodology): “We perform translation between synthetic images in the SYNTHIA dataset [83] and real-world images in the Cityscape dataset [84].”
Learning To Simulate (2018, background): “It is, thus, natural for recent works to explore simulation as a cheaper alternative to human annotation (Gaidon et al., 2016; Ros et al., 2016; Richter et al., 2016).”
RenderGAN: Generating Realistic Labeled Data (2016, neutral): “The DCNNs are evaluated on the mean Hamming distance (MHD) i.e. the expected value of bits decoded wrong.”
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (2019, background): “…et al., 2018] or collecting real [Wen et al., 2015, Yu et al., 2018, Che et al., 2019, Caesar et al., 2019] and artiﬁcial [Gaidon et al., 2016, Ros et al., 2016, Richter et al., 2017, Johnson-Roberson et al., 2017] datasets with varying weather conditions, no general solution towards the…”
Unsupervised Model Adaptation for Continual Semantic Segmentation (2020, methodology): “2016) and the SYNTHIA (Ros et al. 2016)→Cityscapes benchmark UDA tasks for semantic segmentation.”
CARLA: An Open Urban Driving Simulator (2017, background): “Custom simulation setups are commonly used to train and benchmark robotic vision systems [2, 9, 10, 11, 20, 25, 27, 29].”
AI2-THOR: An Interactive 3D Environment for Visual AI (2017, background): “SYNTHIA [16], Virtual KITTI [9], TORCS [21], and CARLA [7] provide synthetic data for autonomous driving.”
Target-driven visual navigation in indoor scenes using deep reinforcement learning (2016, methodology): “[30] propose a new Screenshots of our framework and other simulated learning frameworks: ALE [36], ViZDoom [37], UETorch [38], Project Malmo [39], SceneNet [40], TORCS [41], SYNTHIA [42], Virtual KITTI [43]. search algorithm based on the integration of Monte-Carlo tree search with deep RL that beats”
CyCADA: Cycle-Consistent Adversarial Domain Adaptation (2017, methodology): “For our ﬁrst evaluation, we consider the SYNTHIA dataset (Ros et al., 2016a), which contains synthetic renderings of urban scenes.”
IQA: Visual Question Answering in Interactive Environments (2017, background): “Visual Learning by Simulation: There has been an increased use of simulated environments and game platforms to train computer vision systems to perform tasks such as learning the dynamics of the world [47, 48, 70], semantic segmentation [18], pedestrian detection [43], pose estimation [53] and urban”
nuScenes: A Multimodal Dataset for Autonomous Driving (2019, methodology): “tion labels and [4, 38] providing raw data without semantic labels. An alternative to collecting real-world multimodal driving data is by generating synthetic data via simulation. CARLA [16], SYNTHIA [47], and Virtual KITTI [21] simulate virtual cities using game engines. Playing for Benchmarks [45]”
Visual Semantic Planning Using Deep Successor Representations (2017, methodology): “Computer games and simulated platforms have been used for training perceptual tasks, such as semantic segmentation [18], pedestrian detection [33], pose estimation [38], and urban driving [5, 41, 42, 45].”