Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
This provides a synthetic dataset for computer vision researchers to analyze model performance factors like distance and occlusion, though it is incremental as it builds on existing synthetic data approaches.
The authors introduced Synscapes, a photorealistic synthetic dataset for street scene parsing, and demonstrated state-of-the-art results for training and validation while enabling new analyses of model behavior across real and synthetic data.
We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis. We study the behavior of networks trained on real data when performing inference on synthetic data: a key factor in determining the equivalence of simulation environments. We also compare the behavior of networks trained on synthetic data and evaluated on real-world data. Additionally, by analyzing pre-trained, existing segmentation and detection models, we illustrate how uncorrelated images along with a detailed set of annotations open up new avenues for analysis of computer vision systems, providing fine-grain information about how a model's performance changes according to factors such as distance, occlusion and relative object orientation.