If your CUDA toolkit is older than 11, then you will need to install CUB as follows: conda install -c bottler nvidiacub

Since CUDA 11, CUB is shipped with the toolkit. To install the main library, simply run pip install . in the root directory.

See opt/opt.py

./launch.sh <exp_name> <GPU_id> <data_dir>

NOTE: can no longer use sh

See opt/render_imgs.py

(in opt/) python render_imgs.py <CHECKPOINT.npz> <data_dir>

Including evaluation, ablations, and hypertuning (based on the task_manager one from PlenOctrees) See opt/autotune.py. Configs in opt/tasks/*.json

Automatic eval: python autotune.py -g '<space delimited GPU ids>' tasks/eval.json. Configs in opt/tasks/*.json

First make sure you have colmap installed. Then

(in opt/) bash scripts/proc_colmap.sh <img_dir>

Where <img_dir> should be a directory directly containing png/jpg images from a normal perspective camera. For custom datasets we adopt a data format similar to that in NSVF https://github.com/facebookresearch/NSVF

You should be able to use this dataset directly afterwards. The format will be auto-detected.

To view the data use: python scripts/view_data.py <img_dir>

This should launch a server at localhost:8889

You may notice that this CUDA extension takes forever to install. A suggestion is using ninja. On Ubuntu, install it with sudo apt install ninja-build. Then set the environment variable MAX_JOBS to the number of CPUS to use in parallel (e.g. 12) in your shell startup script. This will enable parallel compilation and significantly improve iteration speed.