Official PyTorch implementation of the paper:
Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields
ICCV 2023
Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, Yuewen Ma
lego.mp4
Instant-ngp (left) suffers from aliasing in distant or low-resolution views and blurriness in close-up shots, while Tri-MipRF (right) renders both fine-grained details in close-ups and high-fidelity zoomed-out images.
To render a pixel, we emit a cone from the camera’s projection center to the pixel on the image plane, and then we cast a set of spheres inside the cone. Next, the spheres are orthogonally projected on the three planes and featurized by our Tri-Mip encoding. After that the feature vector is fed into the tiny MLP to non-linearly map to density and color. Finally, the density and color of the spheres are integrated using volume rendering to produce final color for the pixel.
Our Tri-MipRF achieves state-of-the-art rendering quality while can be reconstructed efficiently, compared with cutting-edge radiance fields methods, e.g., NeRF, MipNeRF, Plenoxels, TensoRF, and Instant-ngp. Equipping Instant-ngp with super-sampling (named Instant-ngp↑5×) improves the rendering quality to a certain extent but significantly slows down the reconstruction.
Please install the following dependencies first
And then install the following dependencies using pip
pip3 install av==9.2.0 \
beautifulsoup4==4.11.1 \
entrypoints==0.4 \
gdown==4.5.1 \
gin-config==0.5.0 \
h5py==3.7.0 \
imageio==2.21.1 \
imageio-ffmpeg \
ipython==7.19.0 \
kornia==0.6.8 \
loguru==0.6.0 \
lpips==0.1.4 \
mediapy==1.1.0 \
mmcv==1.6.2 \
ninja==1.10.2.3 \
numpy==1.23.3 \
open3d==0.16.0 \
opencv-python==4.6.0.66 \
pandas==1.5.0 \
Pillow==9.2.0 \
plotly==5.7.0 \
pycurl==7.43.0.6 \
PyMCubes==0.1.2 \
pyransac3d==0.6.0 \
PyYAML==6.0 \
rich==12.6.0 \
scipy==1.9.2 \
tensorboard==2.9.0 \
torch-fidelity==0.3.0 \
torchmetrics==0.10.0 \
torchtyping==0.1.4 \
tqdm==4.64.1 \
tyro==0.3.25 \
appdirs \
nerfacc==0.3.5 \
plyfile \
scikit-image \
trimesh \
torch_efficient_distloss \
umsgpack \
pyngrok \
cryptography==39.0.2 \
omegaconf==2.2.3 \
segmentation-refinement \
xatlas \
protobuf==3.20.0 \
jinja2 \
click==8.1.7 \
tensorboardx \
termcolorPlease download and unzip nerf_synthetic.zip from the NeRF official Google Drive.
Please generate it by
python scripts/convert_blender_data.py --blenderdir /path/to/nerf_synthetic --outdir /path/to/nerf_synthetic_multiscalepython main.py --ginc config_files/ms_blender/TriMipRF.gin -
Release source code.
If you find the code useful for your work, please star this repo and consider citing:
@inproceedings{hu2023Tri-MipRF,
author = {Hu, Wenbo and Wang, Yuling and Ma, Lin and Yang, Bangbang and Gao, Lin and Liu, Xiao and Ma, Yuewen},
title = {Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields},
booktitle = {ICCV},
year = {2023}
}
