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Found 6 papers, 1 papers with code
Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs
Image synthesis driven by computer graphics achieved recently a remarkable realism, yet synthetic image data generated this way reveals a significant domain gap with respect to real-world data.
Lidar Upsampling with Sliced Wasserstein Distance
This in turn enables our method to employ a one-stage upsampling paradigm without the need for coarse and fine reconstruction.
Attention-based Adversarial Appearance Learning of Augmented Pedestrians
Synthetic data became already an essential component of machine learning-based perception in the field of autonomous driving.
KLIEP-based Density Ratio Estimation for Semantically Consistent Synthetic to Real Images Adaptation in Urban Traffic Scenes
Synthetic data has been applied in many deep learning based computer vision tasks.
Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation
Such performance drops are commonly attributed to the domain gap between real and synthetic data.
Automated Scene Flow Data Generation for Training and Verification
Scene flow describes the 3D position as well as the 3D motion of each pixel in an image.
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