Motion++

Summary

The MOTION++ project extends the MOTION solution by introducing advanced 3D Gaussian Splatting techniques for more accurate inspection of vehicle exteriors. It relies on the fusion of RGB data and depth maps within an innovative cloud-based data collection and processing system, enhanced by dynamic resource management through AerOS. The solution delivers realistic 3D reconstructions, novel view synthesis, and real-time analysis, with potential applications extending beyond the automotive sector, including industry and construction.

Goal

The MOTION++ project aimed to advance automated vehicle inspection technologies by building upon the existing MOTION solution and introducing cutting-edge 3D modeling techniques. At its core, the project sought to improve the accuracy, scalability, and efficiency of surface inspections through the integration of innovative data fusion and cloud-based processing.

To achieve this, the project set out to:

  • Enhance inspection precision using advanced 3D Gaussian Splatting techniques.
  • Fuse RGB imagery and depth maps to create more detailed and reliable representations of vehicle exteriors.
  • Develop a cloud-based data processing framework, supported by AerOS dynamic resource management for scalable performance.
  • Deliver realistic 3D reconstructions, novel view synthesis, and real-time analysis to support inspection and visualization.
  • Explore wider applications of the solution beyond the automotive sector, including industrial monitoring and construction.

Methodology

The methodology combined advanced rendering techniques with robust cloud-based processing to deliver scalable and high-quality results. The process began with the acquisition and fusion of RGB and depth data, ensuring that the visual and geometric information of vehicle surfaces was captured with accuracy.

The workflow included:

  • 3D Gaussian Splatting, applied to generate precise 3D models and enable novel view synthesis for inspections from multiple perspectives.
  • Cloud-based collection and processing, providing the infrastructure for efficient handling of large datasets.
  • Dynamic resource management through AerOS, ensuring that computational resources were allocated effectively according to demand.

The final outcome consisted of realistic 3D reconstructions and real-time analysis capabilities, validated for vehicle inspections but also adaptable for broader use cases in industry and construction.