Zike Yan, Haoxiang Yang, Hongbin Zha
Our environment has been tested on Ubuntu 18.04 (CUDA 10.8 with RTX2080Ti) & 20.04 (CUDA 11.8 with RTX4080).
Clone the repo and create conda environment
git clone --recurse-submodules [email protected]:ZikeYan/activeINR.git && cd activeINR
# create conda env
conda env create -f environment.yml
conda activate activeINR
Install pytorch by following the instructions. For torch 2.0.1 with CUDA version 11.8:
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
pip install -e .
Habitat-lab and habitat-sim need to be installed for simulation. We use v0.1.7 (git checkout tags/v0.1.7)
and install the habitat-sim with the flag --with-cuda
.
pip install -e habitat-lab
python habitat-sim/setup.py install --with-cuda
To run the active mapping in the simulated environment, Gibson dataset for Habitat-sim and the Matterport3D dataset should be downloaded. The directory for the downloaded data should be specified in the config file of activeINR/train/configs/gibson.json
via the key root
.
We adopt the DDPPO for point-goal navigation. All pre-trained models can be found here. The model should be placed in activeINR/local_policy_models
and specified in the config file of activeINR/train/configs/gibson.json
via the key planner
.
To run Active Neural Mapping on the Denmark
scene of Gibson dataset, run the following command.
python activeINR/train/vis_exploration.py --config activeINR/train/configs/mp3d_big.json --scene_id 2azQ1b91cZZ
The logs will be saved in the ./activeINR/train/logs/
folder with actions, mesh file, checkpoints of the neural map, etc.
The mesh quality and the exploration coverage can be evaluated through the following manuscript:
python activeINR/eval/eval_action.py --config activeINR/train/configs/mp3d_big.json --scene_id 2azQ1b91cZZ
python eval/eval_mesh.py
The repo is still under construction, thanks for your patience.
- Running with a live camera in ROS.
- BALD implementation.
- Loss landscape visualization.
- [ ]
Our code is partially based on iSDF and UPEN. We thank the authors for making these codes publicly available.
@inproceedings{Yan2023iccv,
title={Active Neural Mapping},
author={Yan, Zike and Yang, Haoxiang and Zha, Hongbin},
booktitle={Intl. Conf. on Computer Vision (ICCV)},
year={2023}
}