Road layout randomization on semantic labels produces synthetic training pairs that improve mIoU for rare road marking classes by over 12 percentage points in real-world urban deployment while retaining performance on other classes.
Simulating LiDAR point cloud for autonomous driving using real-world scenes and traffic flows,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Synthetic data can partially substitute for real data in object detection training, with performance tied to domain similarity and the volume of real data included.
citing papers explorer
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Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
Road layout randomization on semantic labels produces synthetic training pairs that improve mIoU for rare road marking classes by over 12 percentage points in real-world urban deployment while retaining performance on other classes.
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How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Synthetic data can partially substitute for real data in object detection training, with performance tied to domain similarity and the volume of real data included.