Maximilian Denninger and Rudolph Triebel
Accepted paper at ECCV 2020. paper, short-video, long-video
The author (Maximilian Denninger) gave a talk about the paper, which can be found here.
We present a novel approach to infer volumetric reconstructions from a single viewport, based only on a RGB image and a reconstructed normal image. To overcome the problem of reconstructing regions in 3D that are occluded in the 2D image, we propose to learn this information from synthetically generated high-resolution data. To do this, we introduce a deep network architecture that is specifically designed for volumetric TSDF data by featuring a specific tree net architecture. Our framework can handle a 3D resolution of 512³ by introducing a dedicated compression technique based on a modified autoencoder. Furthermore, we introduce a novel loss shaping technique for 3D data that guides the learning process towards regions where free and occupied space are close to each other. As we show in experiments on synthetic and realistic benchmark data, this leads to very good reconstruction results, both visually and in terms of quantitative measures.
This repository contains everything necessary to reproduce the results presented in our paper. This includes the generation of the data and the training of our model. Be aware, that the generation of the data is time consuming as each process is optimized to the maximum but still billions of truncated signed distance values and weights have to be calculated. Including of course all the color and normals images. The data used for the training of our model was after compression around 1 TB big.
As SUNCG is not longer available, we can not upload the data, we used for training as it falls under the the SUNCG blocking. If you do not have access to the SUNCG dataset, you can try using the 3D-Front dataset and change the code to match this new dataset.
If you find our work useful, please cite us with:
@inproceedings{denninger2020,
title={3D Scene Reconstruction from a Single Viewport},
author={Denninger, Maximilian and Triebel, Rudolph},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
year={2020}
}
Before you execute any of the modules in this project, please install the conda environment:
conda env create -f environment.ymlThis will create the SingleViewReconstruction environment, you can use it by:
conda activate SingleViewReconstructionThis uses Tensorflow 1.15 with python 3.7. This also includes some OpenGL packages for the visualizer.
There is a script, which provides a full run of the BlenderProc pipeline, you will need the "SingleViewReconstruction" environment.
But, be aware before you executed this script. That it will execute a lot of code and download a lot of stuff to your PC.
This program will download BlenderProc and then afterwards blender. It will also download the SceneNet dataset and the corresponding texture lib used by SceneNet.
It will render some color & normal images for the pipeline and will also generate a true output voxelgrid to compare the results to best possible.
Before running, this make sure that you adapt the SDFGen/CMakeLists.txt file. See this README.md.
python run_on_example_scenes_from_scenenet.pyThis will take a while and afterwards you can look at the generated scene with:
python TSDFRenderer/visualize_tsdf.py BlenderProc/output_dir/output_0.hdf5This is a quick overview over the data generation process, it is all based on the SUNCG house files.
- The SUNCG house.json file is converted with the SUNCGToolBox in a house.obj and camerapositions file, for more information: SUNCG
- Then, these two files are used to generate the TSDF voxelgrids, for more information: SDFGen
- The voxelgrid is used to calculate the loss weights via the LossCalculatorTSDF
- They are used to first the train an autoencoder and then compress the 512³ voxelgrids down to a size of 32³x64, which we call encoded. See CompressionAutoEncoder.
- Now only the color & normal images are missing, for that we use BlenderProc with the config file defined in here.
These are then combined with this script to several tf records, which are then used to train our SingleViewReconstruction network.
We provide a script to easily download all models trained in this approach:
- The SingleViewReconstruction model
- The Autoencoder Compression Model CompressionAutoEncoder
- The Normal Generation Model UNetNormalGen
python download_models.py
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