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DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer
arXiv – CS AI|Yuxuan Zhang, Katar\'ina T\'othov\'a, Zian Wang, Kangxue Yin, Haithem Turki, Riccardo de Lutio, Yen-Yu Chang, Or Litany, Sanja Fidler, Zan Gojcic||5 views
🤖AI Summary
Researchers introduce DiffusionHarmonizer, an AI framework that enhances neural reconstruction simulations for autonomous robots by converting multi-step image diffusion models into single-step enhancers. The system addresses artifacts in NeRF and 3D Gaussian Splatting methods while improving realism for applications like self-driving vehicle simulation.
Key Takeaways
- →DiffusionHarmonizer transforms imperfect neural reconstruction renderings into temporally consistent, realistic outputs for autonomous robot simulation.
- →The framework converts pretrained multi-step image diffusion models into single-step enhancers that can run on a single GPU.
- →Current methods like NeRF and 3D Gaussian Splatting produce visual artifacts and fail to realistically integrate dynamic objects from different scenes.
- →A custom data curation pipeline constructs synthetic-real pairs emphasizing appearance harmonization and artifact correction.
- →The system aims to significantly elevate simulation fidelity in both research and production environments for autonomous vehicles.
#diffusion-models#neural-reconstruction#autonomous-vehicles#simulation#nerf#gaussian-splatting#computer-vision#robotics
Read Original →via arXiv – CS AI
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