Streaming Radiance Fields for 3D Video Synthesis

Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan

Alibaba Group

Citation:

@article{li2022streaming,
  title={Streaming radiance fields for 3d video synthesis},
  author={Li, Lingzhi and Shen, Zhen and Wang, Zhongshu and Shen, Li and Tan, Ping},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={13485--13498},
  year={2022}
}

arXiv: https://arxiv.org/abs/2210.14831

StreamRF.mp4

Due to size limit, this is a downsampled video, check full resolution video here.

Dataset

Meet Room Dataset: ModelScope魔搭 ，Google Drive

We will add more data in ModelScope. (我们会在魔搭里更新更多的数据，敬请关注)

N3DV Dataset: https://github.com/facebookresearch/Neural_3D_Video

Training StreamRF

Following the setup of the orginal plenoxels' repository

For each scene, extract frames from every video, and arrange them into the following structure:

python prepare_dataset.py <video_dir>

<data_dir>
  ├── 0000  
  |   ├── poses_bounds.npy  
  |   └── images
  |       └── cam[00/01/02/.../20].png
  ...
  └── 0299 
      ├── poses_bounds.npy  
      └── images
          └── cam[00/01/02/.../20].png

We provide the pose_bounds.npy of both dataset in the Meet Room Dataset's link. If you wants to generate poses_bounds.npy for yourself check DS-NeRF's repo.

Meet Room Dataset

1. Initialize the first frame model

python opt.py  -t <log_dir> <data_dir>/0000 -c configs/meetroom_init.json --scale 1.0

2. Train the pilot model

python train_video_n3dv_pilot.py -t <log_dir> <data_dir> -c configs/meetroom.json --batch_size 20000   --pretrained <pretrained_ckpt>  --n_iters 1000    --lr_sigma 0.3  --lr_sigma_final 0.3  --lr_sh 1e-2 --lr_sh_final 1e-4 --lr_sigma_decay_steps 1000 --lr_sh_decay_steps 1000   --frame_end 300 --fps 30 --train_use_all 0 --scale 1.0 --sh_keep_thres 1.0 --sh_prune_thres 0.1 --performance_mode  --dilate_rate_before 1 --dilate_rate_after 1 --stop_thres 0.01  --compress_saving --save_delta   --pilot_factor 2 

3. Train the full model

python train_video_n3dv_full.py -t <log_dir> <data_dir> -c configs/meetroom_full.json --batch_size 20000   --pretrained <pretrained_ckpt>  --n_iters 500    --lr_sigma 1.0 --lr_sigma_final 1.0  --lr_sh 1e-2 --lr_sh_final 1e-2 --lr_sigma_decay_steps 500 --lr_sh_decay_steps 500   --frame_end 300 --fps 30 --train_use_all 0 --scale 1.0 --sh_keep_thres 1.5 --sh_prune_thres 0.3 --performance_mode  --dilate_rate_before 2 --dilate_rate_after 2    --compress_saving --save_delta  --apply_narrow_band 

N3DV Dataset

1. Initialize the first frame model

python opt.py  -t <log_dir> <data_dir>/0000 -c configs/init_ablation/n3dv_init.json --offset 500 --scale 0.5 --nosphereinit 

2. Train the pilot model

python train_video_n3dv_pilot.py -t <log_dir> <data_dir> -c configs/n3dv.json --batch_size 20000    --pretrained <pretrained_ckpt>  --n_iters 750    --lr_sigma 1.0  --lr_sigma_final 1.0  --lr_sh 1e-2 --lr_sh_final 1e-3 --lr_sigma_decay_steps 750 --lr_sh_decay_steps 750   --frame_end 300 --fps 30 --train_use_all 0 --offset 750  --scale 0.5 --sh_keep_thres 0.5 --sh_prune_thres 0.1 --performance_mode  --dilate_rate_before 1 --dilate_rate_after 1 --stop_thres 0.01  --compress_saving --save_delta   --pilot_factor 2 

3. Train the full model

python train_video_n3dv_full.py -t <log_dir> <data_dir> -c configs/n3dv_full.json --batch_size 20000   --pretrained  <pretrained_ckpt>  --n_iters 500    --lr_sigma 1.0 --lr_sigma_final 1.0  --lr_sh 1e-2 --lr_sh_final 3e-3 --lr_sigma_decay_steps 500 --lr_sh_decay_steps 300   --frame_end 300 --fps 30 --train_use_all 0 --offset 1500  --scale 0.5 --sh_keep_thres 1.0 --sh_prune_thres 0.2 --performance_mode  --dilate_rate_before 2 --dilate_rate_after 2  --stop_thres 0.01  --compress_saving --save_delta  --apply_narrow_band

We can change the tv loss weight for better video quality but lower PSNR

3. Train the pilot model

python train_video_n3dv_full.py -t <log_dir> <data_dir> -c configs/n3dv_full_hightv.json --batch_size 20000   --pretrained  <pretrained_ckpt>  --n_iters 500    --lr_sigma 1.0 --lr_sigma_final 1.0  --lr_sh 1e-2 --lr_sh_final 3e-3 --lr_sigma_decay_steps 500 --lr_sh_decay_steps 300   --frame_end 300 --fps 30 --train_use_all 0 --offset 1500  --scale 0.5 --sh_keep_thres 1.0 --sh_prune_thres 0.2 --performance_mode  --dilate_rate_before 2 --dilate_rate_after 2  --stop_thres 0.01  --compress_saving --save_delta  --apply_narrow_band
```bash

## Testing StreamRF

For Meet Room Dataset：
```bash
python render_delta.py  -t <log_dir> <data_dir> -c configs/meetroom_full.json --batch_size 20000    --pretrained <pretrained_ckpt>  --frame_end 300 --fps 30 --scale 1.0 --performance_mode  

For N3DV Dataset：

python render_delta.py  -t <log_dir> <data_dir> -c configs/n3dv_full.json --batch_size 20000    --pretrained <pretrained_ckpt>  --frame_end 300 --fps 30 --scale 0.5 --performance_mode