This repo is for project combind DROID-SLAM and Metric3D, taking metric depth to improve the performance of DROID-SLAM in monocular mode.
# clone the repo with '--recursive' to get the submodules
# or run 'git submodule update --init --recursive' after cloning
cd droid_metric
# create conda env
conda create -n droid_metric python=3.9
conda activate droid_metric
# install pytorch (other versions may also work)
conda install pytorch==2.0.0 torchvision==0.15.0 pytorch-cuda=11.7 -c pytorch -c nvidia
# install python packages
pip install -r requirements.txt
# install droid-slam-backend
cd modules/droid_slam
python setup.py install
cd ../..Download DROID-SLAM and Metric3D pretrained model running
python download_models.pyDownload ADVIO dataset running
python download_dataset.pyFor camera calibration, check scripts/calib.py
For video sampling, check scripts/sample.py
## depth estimate
python depth.py --rgb $/path/to/rgb/dir --out $/path/to/output --intr $/path/to/intrinsic/file
# for more options, check `depth.py`
## droid-slam
python run.py --rgb $/path/to/rgb/dir --depth $/path/to/depth/dir --intr $/path/to/intrinsic/file --viz
# for more options, check `run.py`
## mesh recon
python mesh.py --rgb $/path/to/rgb/dir --depth $/path/to/depth/dir --poses $/path/to/pose/dir --intr $/path/to/intrinsic/file --save $/path/to/output/mesh/ply
# for more options, check `mesh.py`## test depth estimate, droid slam and mesh reconstruction for rgb image sequence
python -m scripts.test_seq --rgb $/path/to/rgb/dir --depth $/path/to/depth/dir --poses $/path/to/pose/dir --save $/path/to/output/mesh/ply --intr $/path/to/intrinsic/file --vizTested on part of ICL-NUIM and ADVIO dataset. droid_D means DROID-SLAM with Metric3D, droid mean oroginal DROID-SLAM and vslam means the OpenVSLAM framework. Notice that vslam method get lost on ICL-OfficeRoom-1 and all sequences of ADVIO.
(some of the trajectories seem not aligned correctly, sorry for that.)
