Scan-RL: Next-Best View Policy for 3D Reconstruction

Next-Best View Policy for 3D Reconstruction to be presented at 2020 ECCV Wokshop.

Peralta, D., Casimiro, J., Nilles, A.M., Aguilar, J.A., Atienza, R., and Cajote, R. "Next-Best View Policy for 3D Reconstruction." European Conference on Computer Vision (ECCV) Workshops, 2020.

Scan-RL code implementation from the paper Next-Best View Policy for 3D Reconstruction. The Houses3K dataset used in this paper can be found in this link.

Scan-RL

Diagram

Setting up the Environment

Necessary Python packages can be found in python_requirements.txt.
To install the environments, you need to install our fork of gym-unrealcv. Additional instructions are included there.

Single House Policy Experiment

Weights
Circular baseline script can be found here.
Sample Usage

Training (Discrete Action Space)

python main_unreal.py --nb_episodes 500 --batch_size 32 --epsilon_decay 0.999 --epsilon 1.0 --save_interval 20 --consecutive_frames 3 --type DDQN --env DepthFusionBGray-v0

Training (Continuous Action Space)

python main_unreal.py --nb_episodes 500 --batch_size 32 --epsilon_decay 0.999 --epsilon 1.0 --save_interval 20 --consecutive_frames 6 --type DDPG --env DepthFusionBGrayContinuous-v0

Testing

python load_and_run_unreal.py --type DDQN --consecutive_frames 6 --model_path '/hdd/AIRSCAN/sfm_results/RL_VP/new_baselines/bat6/2dist_45az_3elev/models/DDQN_ENV_DepthFusionBGray-v0_NB_EP_1000_BS_32_LR_0.00025_ep_10000.h5' --epsilon 0.0

Multiple Houses Policy Experiment (Geometry Split)

Weights
Circular baseline script can be found here.
Sample Usage

Training

python main_unreal.py --nb_episodes 2500 --batch_size 10 --epsilon_decay 0.999 --epsilon 1.0 --save_interval 20 --consecutive_frames 6 --type DDQN --env DepthFusionBGrayMultHouseRand-v0

Testing

python main_unreal.py --nb_episodes 2500 --batch_size 10 --epsilon_decay 0.999 --epsilon 1.0 --save_interval 20 --consecutive_frames 6 --type DDQN --env DepthFusionBGrayMultHouseRand-v0

Non-House Target Model (Stanford Bunny) Experiment

Weights
Sample Usage

python load_and_run_unreal.py --model_path '/home/daryl/gym-unrealcv/new_bunny_3pen_89cov/models/DDQN_ENV_Bunny-v0_NB_EP_1000_BS_32_LR_0.00025_ep_10000.h5' --consecutive_frames 6 --type DDQN --env Bunny-v0 --epsilon 0.0

Citation

Please cite our paper if you find our work useful.

@article{peralta2020next,
  title={Next-Best View Policy for 3D Reconstruction},
  author={Peralta, Daryl and Casimiro, Joel and Nilles, Aldrin Michael and Aguilar, Justine Aletta and Atienza, Rowel and Cajote, Rhandley},
  journal={arXiv preprint arXiv:2008.12664},
  year={2020}
}

References

The RL implementation was based on this repo. Gym environments are based on UnrealCV and gym-unrealcv.

darylperalta/ScanRL