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Deep 3D Optimization

Elena Camuffo, Federica Battisti, Francesco Pham, Simone Milani - EUVIP 2022 [paper]

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Abstract

The growing diffusion of immersive and interactive applications is posing new challenges in the multimedia processing chain. When dealing with AR and VR applications, the most relevant aspects to consider are the (1) quality of the visualized 3D objects and (2) the fluidity in the visualization in case the user is moving in the environment. In this framework, we propose a deep learning based approach that estimates the optimal model parameters to be used in relation to the viewer’s movement and the model characteristics and quality. The performed tests show the effectiveness of the proposed approach.

General Requirements

As general requirements to install this project you need a .conda environment with Python 3.7 (with Pytorch 1.11) and Unity3D 2020.1.11.f1. Move in PythonScripts folder and install all the packages using the command:

pip install -r PythonScripts/requirements.txt

(1) Positions generation

Generate a set of uniformly distributed poisitions, around (0,0,0). Use:

  • generate_clustered_positions function to generate position pairs for an inter-view environment.
  • generate_positions function to generate position pairs for an intra-view environment.
python random_walk_uniform.py

The generated positions are stored in positions.txt.

(2) Scene Setup

The Unity environment is contained in the Assets folder. Import your models with different LODs here.

  • To capture the renders from different viewpoints use the scene CameraPathScreenshots.unity
  • To compute intra-view SSIM use the scene SsimPredict.unity
  • To obtain OTC-projections use the scene SurfaceCountPorjections.unity

(3) Training the Network

To generate your own dataset use the script build_dataset.py or use ours dataset.json. To train the network run the command:

python dynamic_ssim_multimodel/multimodel_predictor.py

Credits

If you consider our work useful, please consider citing:

  @article{camuffo2022deep,
    title={Deep 3D Model Optimization for Immersive and Interactive Applications},
    author={Camuffo, Elena and Battisti, Federica and Pham, Francesco and Milani, Simone},
    journal={European Workshop on Visual Information Processing (EUVIP)},
    year={2022}
    }