mlsanityData to 7 Oct 2026

Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models

arXiv 2305.14621 ↗First posted 24 May 2023cs.CVcited by 1

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Abstract (arXiv)

When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data. We investigate specifically the impact of object placement distribution, keeping all other aspects of synthetic data fixed. Our experiment, training a 3D vehicle detection model in CARLA and testing on KITTI, demonstrates a substantial improvement resulting from improving the object placement distribution.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

16 of 23 references.

References · 16

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  3. 2018Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomizationcited by 292
  4. 2016CAD2RL: Real Single-Image Flight without a Single Real Imagecited by 227
  5. 2021Probabilistic and Geometric Depth: Detecting Objects in Perspectivecited by 190
  6. 2019Meta-Sim: Learning to Generate Synthetic Datasetscited by 128
  7. 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
  8. 2021Learning to Track with Object Permanencecited by 117
  9. 2018Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Datacited by 94
  10. 2021SceneGen: Learning to Generate Realistic Traffic Scenescited by 83
  11. 2020Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generationcited by 58
  12. 2017Synthesizing Training Data for Object Detection in Indoor Scenescited by 48
4 more
  1. 2019How much real data do we actually need: Analyzing object detection performance using synthetic and real datacited by 29
  2. 2018Domain Randomization for Scene-Specific Car Detection and Pose Estimationcited by 23
  3. 2020Self-Supervised Real-to-Sim Scene Generationcited by 13
  4. 2022Conditional Permutation Invariant Flowscited by 6

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  1. 2025Prior Distribution and Model Confidencecited by 1