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Updated: Jul 13, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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GPU Rasterization-Based 3D LiDAR Simulation for Deep Learning.

Leon Denis1,2, Remco Royen1,2, Quentin Bolsée1,2

  • 1Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a GPU-accelerated simulator for generating high-quality, labeled data for Time-of-Flight sensors like LiDAR. A novel loss function reduces annotation needs for deep learning applications.

Keywords:
GPULiDARdata generationneural networkssimulation

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Sensor Technology

Background:

  • High-quality data and annotation are crucial for deep learning but acquiring them is challenging.
  • Time-of-Flight (ToF) sensors, including LiDAR, require specific data for effective deep learning model training.
  • Existing data generation methods may be time-consuming or lack compatibility with real-time rendering engines.

Purpose of the Study:

  • To present a GPU-accelerated simulator for generating high-quality, perfectly labeled data for any Time-of-Flight sensor.
  • To develop generic algorithms that mimic unique sensor sampling patterns.
  • To reduce the data acquisition and annotation burden for deep learning applications.

Main Methods:

  • Utilizing a GPU-accelerated simulator that leverages the 3D graphics pipeline for efficient data generation.
  • Implementing generic algorithms to accurately replicate sensor-specific sampling patterns.
  • Introducing a novel loss function that integrates partially annotated real data to bridge the simulation-reality gap.

Main Results:

  • The simulator significantly decreases data generation time while maintaining compatibility with real-time rendering engines.
  • Trained neural networks for denoising and semantic segmentation using simulated data validated the simulator's effectiveness.
  • The novel loss function enabled learning from unlabeled classes in real data, substantially reducing annotation efforts.

Conclusions:

  • The developed simulator effectively addresses the data acquisition challenges in deep learning for ToF sensors.
  • The approach offers a viable solution for creating large, perfectly labeled datasets essential for training robust deep learning models.
  • The integration of simulated and real data via the novel loss function enhances model performance and reduces manual annotation requirements.