Fisheye-GS

ECCV 2024 Workshop NFBCC

arxiv

Abstract: Recently, 3D Gaussian Splatting (3DGS) has garnered attention for its high fidelity and real-time rendering. However, adapting 3DGS to different camera models, particularly fisheye lenses, poses challenges due to the unique 3D to 2D projection calculation. Additionally, there are inefficiencies in the tile-based splatting, especially for the extreme curvature and wide field of view of fisheye lenses, which are crucial for its broader real-life applications. To tackle these challenges, we introduce Fisheye-GS. This innovative method recalculates the projection transformation and its gradients for fisheye cameras. Our approach can be seamlessly integrated as a module into other efficient 3D rendering methods, emphasizing its extensibility, lightweight nature, and modular design. Since we only modified the projection component, it can also be easily adapted for use with different camera models. Compared to methods that train after undistortion, our approach demonstrates a clear improvement in visual quality.

TODO

• Release paper ✔
• Release Fisheye-GS for FlashGS
• Release the dataset we use
• Release panorama

Hardware Requirements

• CUDA-ready GPU with Compute Capability 7.0+
• 24 GB VRAM (to train to paper evaluation quality)

Software Requirements

• Conda (recommended for easy setup)
• C++ Compiler for PyTorch extensions
• CUDA SDK 11 for PyTorch extensions
• C++ Compiler and CUDA SDK must be compatible

Setup

conda env create --file environment.yml
conda activate fisheye_gs

Prepare Training Data on Scannet++ Dataset

Undistort the distortions excluding the radial distortion from $k_1$

python prepare_scannetpp.py \
    --path <path to your dataset> \
    --src images \
    --dst image_undistorted_fisheye 

Or simply use:

sh scripts/prepare.sh

Training on Scannet++ Dataset

python train.py \
    -s <path to your dataset> \
    -m <path to model to be trained> \
    --images <relative path to images> \
    --colmaps <relative path to colmap file if using dataset> \
    --bs 3 \
    --ds 1 \
    --camera_model <FISHEYE/PINHOLE> \
    --train_random_background 

Or simply use:

sh scripts/train.sh

Rendering on Scannet++ Dataset

python render.py \
    -s <path to your dataset> \
    -m <path to model to be trained> \
    --images <relative path to images> \
    --colmaps <relative path to colmap file if using dataset> \
    --skip_train \
    --camera_model <FISHEYE/PINHOLE> \
    --ds 1 \
    -r 1 

Or simply use

sh scripts/render.sh

Evaluating

python metrics.py \
    -m <path to model to be trained>

Or simply use

sh scripts/eval.sh

License

Please follow the LICENSE of 3D-GS.

Acknowledgement

We thank all authors from 3D-GS for presenting such an excellent work.