name string | hash string | batch string | scale float64 | R_align list | center_mean list |
|---|---|---|---|---|---|
scene_0 | 2b6a721345441b59a2ec614bdcd4c457fc86d1631284219b2561d50cadfad13b | 1K | 0.856 | [
[
-0.55376869,
-0.1932619,
0.80993212
],
[
0.83175109,
-0.08268912,
0.54895594
],
[
-0.03911974,
0.97765654,
0.20653651
]
] | [
-0.0647619,
0.38003285,
0.07235761
] |
This is the repository to download and prepare the MBZUAI campus reconstruction
Installation
TL,DR --> run install.sh (if it's there)
Conda environment
conda create --name nerfstudio -y python=3.8
conda activate nerfstudio
pip install --upgrade pip
CUDA Dependencies
pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
NERFstudio install
pip install nerfstudio
COLMAP install
conda install -y conda-forge::colmap
GLOMAP install
conda install -y conda-forge::glomap
ffmpeg for processing images
conda install conda-forge::ffmpeg
Reproduce results
1. Pre-Process individual videos sequences
python prepare_data.py
2. Reconstruct the campus hierarchically
first run the run_core.sh script. This will reconstruct a base structure, upon which all the other reconstructions will be added to.
This is the most important one.
For the other scenes, for now just copy the run_template.sh file and modify according to the current scane name and file. Usually I run line by line to make sure everything is correct, but theoretically you could just run the entire file.
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