Concise (<1000 locs) and fast implementation of several NeRF techniques. Currently it contains an implementation of vanilla NeRF, K-Planes and Cobafa, accelerated with a single CUDA kernel to compute the weights from 'NeRF equation'.
output_video.mp4
Create a conda environment, install PyTorch, and install requirements.
conda create -n tinynerf python=3.10
conda activate tinynerf
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txtI got the dataset from nerfstudio, running the ns-download-data blender command and then copying it to ./data/.
Run it:
CUDA_VISIBLE_DEVICES=0 python train.py --data data/blender/chair --method kplanes --datatype synthetic --output k_planes --batch_size 512- Vanilla NeRF, K-Planes and Cobafa
- Occupancy grid to accelerate training (based on Instant-NGP but with slightly different decaying method)
- Unbounded and AABB scenes
- Dynamic batches, each iteration process a constant number of samples by packing samples from each ray
- CUDA implementation of NeRF weights computation
- Reproduction of KPlanes results on synthetic dataset
- Reproduction of Cobafa results on synthetic dataset
- Proposal sampling
- COLMAP data loading
- Appearance embedding
These repositories were useful learning resources :