PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion

Last update: Dec 12, 2022

Related tags

Overview

DRO: Deep Recurrent Optimizer for Structure-from-Motion

This is the official PyTorch implementation code for DRO-sfm. For technical details, please refer to:

DRO: Deep Recurrent Optimizer for Structure-from-Motion
Xiaodong Gu*, Weihao Yuan*, Zuozhuo Dai, Chengzhou Tang, Siyu Zhu, Ping Tan
[Paper]

Bibtex

If you find this code useful in your research, please cite:

@article{gu2021dro,
  title={DRO: Deep Recurrent Optimizer for Structure-from-Motion},
  author={Gu, Xiaodong and Yuan, Weihao and Dai, Zuozhuo and Tang, Chengzhou and Zhu, Siyu and Tan, Ping},
  journal={arXiv preprint arXiv:2103.13201},
  year={2021}
}

Install
Datasets
Training
Evaluation
Models

Install

We recommend using nvidia-docker2 to have a reproducible environment.

git clone https://github.com/aliyun/dro-sfm.git
cd dro-sfm
sudo make docker-build
sudo make docker-start-interactive

You can also download the built docker directly from dro-sfm-image.tar

docker load < dro-sfm-image.tar

If you do not use docker, you could create an environment following the steps in the Dockerfile.

# Environment variables
export PYTORCH_VERSION=1.4.0
export TORCHVISION_VERSION=0.5.0
export NCCL_VERSION=2.4.8-1+cuda10.1
export HOROVOD_VERSION=65de4c961d1e5ad2828f2f6c4329072834f27661
# Install NCCL
sudo apt-get install libnccl2=${NCCL_VERSION} libnccl-dev=${NCCL_VERSION}

# Install Open MPI
mkdir /tmp/openmpi && \
    cd /tmp/openmpi && \
    wget https://www.open-mpi.org/software/ompi/v4.0/downloads/openmpi-4.0.0.tar.gz && \
    tar zxf openmpi-4.0.0.tar.gz && \
    cd openmpi-4.0.0 && \
    ./configure --enable-orterun-prefix-by-default && \
    make -j $(nproc) all && \
    make install && \
    ldconfig && \
    rm -rf /tmp/openmpi

# Install PyTorch
pip install torch==${PYTORCH_VERSION} torchvision==${TORCHVISION_VERSION} && ldconfig

# Install horovod (for distributed training)
sudo ldconfig /usr/local/cuda/targets/x86_64-linux/lib/stubs && HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=NCCL HOROVOD_WITH_PYTORCH=1 pip install --no-cache-dir git+https://github.com/horovod/horovod.git@${HOROVOD_VERSION} && sudo ldconfig

To verify that the environment is setup correctly, you can run a simple overfitting test:

# download a tiny subset of KITTI
cd dro-sfm
curl -s https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/dro-sfm/datasets/KITTI_tiny.tar | tar xv -C /data/datasets/kitti/
# in docker
./run.sh "python scripts/train.py configs/overfit_kitti_mf_gt.yaml" log.txt

Datasets

Datasets are assumed to be downloaded in /data/datasets/ (can be a symbolic link).

KITTI

The KITTI (raw) dataset used in our experiments can be downloaded from the KITTI website. For convenience, you can download data from packnet or here

Tiny KITTI

For simple tests, you can download a "tiny" version of KITTI:

Scannet

The Scannet (raw) dataset used in our experiments can be downloaded from the Scannet website. For convenience, you can download data from here

DeMoN

Download DeMoN.

bash download_traindata.sh
python ./dataset/preparation/preparedata_train.py
bash download_testdata.sh
python ./dataset/preparation/preparedata_test.py

Training

Any training, including fine-tuning, can be done by passing either a .yaml config file or a .ckpt model checkpoint to scripts/train.py:

# kitti, checkpoints will saved in ./results/mdoel/
./run.sh 'python scripts/train.py  configs/train_kitti_mf_gt.yaml' logs/kitti_sup.txt
./run.sh 'python scripts/train.py  configs/train_kitti_mf_selfsup.yaml' logs/kitti_selfsup.txt 

# scannet
./run.sh 'python scripts/train.py  configs/train_scannet_mf_gt_view3.yaml' logs/scannet_sup.txt
./run.sh 'python scripts/train.py  configs/train_scannet_mf_selfsup_view3.yaml' logs/scannet_selfsup.txt
./run.sh 'python scripts/train.py  configs/train_scannet_mf_gt_view5.yaml' logs/scannet_sup_view5.txt

# demon
./run.sh 'python scripts/train.py  configs/train_demon_mf_gt.yaml' logs/demon_sup.txt

Evaluation

python scripts/eval.py --checkpoint <checkpoint.ckpt> [--config <config.yaml>]
# example:kitti, results will be saved in results/depth/
python scripts/eval.py --checkpoint ckpt/outdoor_kitti.ckpt --config configs/train_kitti_mf_gt.yaml

You can also directly run inference on a single image or video:

# video or folder
# indoor-scannet 
python scripts/infer_video.py --checkpoint ckpt/indoor_sacnnet.ckpt --input /path/to/video or folder --output /path/to/save_folder --sample_rate 1 --data_type scannet --ply_mode 
 # indoor-general
python scripts/infer_video.py --checkpoint ckpt/indoor_sacnnet.ckpt --input /path/to/video or folder --output /path/to/save_folder --sample_rate 1 --data_type general --ply_mode

# outdoor
python scripts/infer_video.py --checkpoint ckpt/outdoor_kitti.ckpt --input /path/to/video or folder --output /path/to/save_folder --sample_rate 1 --data_type kitti --ply_mode 

# image
python scripts/infer.py --checkpoint <checkpoint.ckpt> --input <image or folder> --output <image or folder>

Models

Model	Abs.Rel.	Sqr.Rel	RMSE	RMSElog	a1	a2	a3	SILog	L1_inv	rot_ang	t_ang	t_cm
Kitti_sup	0.045	0.193	2.570	0.080	0.971	0.994	0.998	0.079	0.003	-	-	-
Kitti_selfsup	0.053	0.346	3.037	0.102	0.962	0.990	0.996	0.101	0.004	-	-	-
scannet_sup	0.053	0.017	0.165	0.080	0.967	0.994	0.998	0.078	0.033	0.472	9.297	1.160
scannet_sup(view5)	0.047	0.014	0.151	0.072	0.976	0.996	0.999	0.071	0.030	0.456	8.502	1.163
scannet_selfsup	0.143	0.345	0.656	0.274	0.896	0.954	0.969	0.272	0.106	0.609	10.779	1.393

Acknowledgements

Thanks to Toyota Research Institute for opening source of excellent work packnet-sfm. Thanks to Zachary Teed for opening source of his excellent work RAFT.

PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion

Related tags

Overview

DRO: Deep Recurrent Optimizer for Structure-from-Motion

Bibtex

Contents

Install

Datasets

KITTI

Tiny KITTI

Scannet

DeMoN

Training

Evaluation

Models

Acknowledgements

Owner

Alibaba Cloud

C3DPO - Canonical 3D Pose Networks for Non-rigid Structure From Motion.

Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

Noise Conditional Score Networks (NeurIPS 2019, Oral)

MGFN: Multi-Graph Fusion Networks for Urban Region Embedding was accepted by IJCAI-2022.

Pytorch implementation for "Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion" (NeurIPS 2021)

GE2340 project source code without credentials.

Large-Scale Unsupervised Object Discovery

This repository contains the code for the paper in EMNLP 2021: "HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression".

Fully convolutional deep neural network to remove transparent overlays from images

A disassembler for the RP2040 Programmable I/O State-machine!

Implementation of Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021)

Bayesian Neural Networks in PyTorch

FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment

Pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"

ArcaneGAN by Alex Spirin

Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification

Identify the emotion of multiple speakers in an Audio Segment

Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV @ CVPR 2021.

A concise but complete implementation of CLIP with various experimental improvements from recent papers

Tweesent-back - Tweesent backend uses fastAPI as the web framework