Awesome

DL-GS

High-Resolution and Few-shot View Synthesis from Asymmetric Dual-lens Inputs, In ECCV 2024. Ruikang Xu, Mingde Yao, Yue Li, Yueyi Zhang, Zhiwei Xiong.

Dependencies

Our Environment is Bulid on the Docker Image from the INRIA lab.
Other Dependencies: BasicSR 1.3.4.9, OpenCV 4.7.0, Scikit-image, CuPy, Open3d, Pillow, Imageio, COLMAP.

Compile CUDA:

cd ./Code
pip install submodules/diff-gaussian-rasterization-confidence
pip install submodules/simple-knn

Data Preparation

The StereoNeRF dataset can be downloaded from this link.

Simulated Dual-lens Scenes:

cd ./SimulatedData && python dualLensSyn.py && python combinWideTele.py

Split Training and Test views:

cd ./SimulatedData && python split_TrainTest.py

Quick Start

1. Consistency-aware Training

Pre-upsample (please download the pretrained HAT for 2x SR):

cd ./Code/SISR && python test.py -opt HAT-S_SRx2_SISR.yml

Run COLMAP for Camera Pose Estimation with Sparse Views and Stereo-fusion-based Initialization:
```
cd ./Code/colmap_sh && sh colmap.sh
```
Training with Two Designed Loss Functions (please download the pretrained MiDas):
```
cd ./Code && sh train_gs.sh
```
Rendering Gaussians:
```
cd ./Code && sh render_gs.sh
```

2. Multi-reference-guided Refinement

Pre-alignment Telephoto Images to Wide-angle Images:
```
cd ./Code/alignTele && sh align.sh
```
Training with Self-learning Loss Functions:
```
cd ./Code && sh train_dlde.sh
```

3. Rendering Full Pipeline:

cd ./Code && sh render_full.sh

TODO List:

We will release our code for the real-captured dataset in the future.

Contact

Any question regarding this work can be addressed to xurk@mail.ustc.edu.cn.

Citation

If you find our work helpful, please cite the following paper.

@inproceedings{Xu_2024_ECCV,
  title={High-Resolution and Few-shot View Synthesis from Asymmetric Dual-lens Inputs},
  author={Xu, Ruikang and Yao, Mingde and Yue, Li and Yueyi, Zhang and Xiong, Zhiwei},
  booktitle={European Conference on Computer Vision},
  year={2024},
  organization={Springer}
}