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CL-NeRF

Official PyTorch implementation for the paper "CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation Video (NeurIPS 2023)".

Prerequisites

  • You can create the environment with: 
    pip install -r requirements.txt

Download Pre-trained Weights

Download the pre-trained models from the drive and unzip them into the project's root directory for later testing. Refer to the directory structure example provided: Kitchen contains the pre-trained NeRF model, and Kitchen_ADD_clnerf is fine-tuned with new images following the ADD operation.:

├── logs 
│   ├── Kitchen
│   │   ├── 200000.tar
│   ├── Kitchen_ADD_clnerf
│   │   ├── 210000_expert.tar
│   │   ├── 210000.tar
│   ├── ...

Download Dataset

Download the datasets from the drive and unzip them into the project's root directory for training. Refer to the provided example for the directory structure:

├── data 
│   ├── Kitchen
│   │   ├── original
│   │   ├── sequential_operation
│   │   ├── single_operation
│   │   │   ├── ADD
│   │   │   ├── DEL
│   │   │   ├── MOV
│   │   │   ├── REP
│   ├── Whiteroom 
│   ├── ...

Test

We provide an example using the ADD operation in the Kitchen dataset. Start by downloading the pre-trained weights and dataset. Then, execute the following script:

bash ./experiments/inference/inference_kitchen_after_ADD_clnerf.sh

When finished, results are saved to logs/Kitchen_ADD_clnerf/renderonly_test_stage1_newtask_209999 and logs/Kitchen_ADD_clnerf/renderonly_test_stage1_oldtask_209999. To use different operations or datasets, replace 'ADD' and 'kitchen' accordingly.

Train

First download the dataset. Then,

bash ./experiments/single_operation/ADD/train_kd_expert_mask_kitchen.sh

Citation

If you utilize the code, datasets, or concepts from our paper in your research, please kindly cite:

@article{wu2024cl,
  title={CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation},
  author={Wu, Xiuzhe and Dai, Peng and Deng, Weipeng and Chen, Handi and Wu, Yang and Cao, Yan-Pei and Shan, Ying and Qi, Xiaojuan},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}