Self-supervised surround-view depth estimation with volumetric feature fusion

Jung-Hee Kim*, Junwha Hur*, Tien Nguyen, and Seong-Gyun Jeong - NeurIPS 2022
Link to the paper: Link

@inproceedings{kimself,
  title={Self-supervised surround-view depth estimation with volumetric feature fusion},
  author={Kim, Jung Hee and Hur, Junhwa and Nguyen, Tien Phuoc and Jeong, Seong-Gyun},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)}
  year = {2022},
}

Introduction

We introduce a volumetric feature representation for self-supervised surround-view depth approach, which not only outputs metric-scale depth and canonical camera motion, but also synthesizes a depth map at a novel viewpoint.

Installation

• Install required libraries using the requirements.txt file.
  (Note that we leverage packent-sfm, dgp as submodules and therefore need to install required libraries related to the submodules.)
• To install both required library and submodules, you need to follow instruction below:

git submodule init
git submodule update
pip install -r requirements.txt

Datasets

DDAD

• DDAD dataset can be downloaded by running:

curl -s https://tri-ml-public.s3.amazonaws.com/github/DDAD/datasets/DDAD.tar 

• Place the dataset in input_data/DDAD/
• We manually created mask image for scene of ddad dataset and are provided in dataset/ddad_mask

NuScenes

• Download NuScenes official dataset
• Place the dataset in input_data/nuscenes/
• Scenes with backward and forward contexts are listed in dataset/nuscenes/
• Scenes with low visibility are filtered in dataset/nuscenes/val.txt

Data should be as follows:

├── input_data
│   ├── DDAD
│   │   ├── ddad_train_val
│   │   ├── ddad_test
│   ├── nuscenes
│   │   ├── maps
│   │   ├── samples
│   │   ├── sweeps
│   │   ├── v1.0-test
|   |   ├── v1.0-trainval

Main Results

Model	Scale	Abs.Rel.	Sq.Rel.	RMSE	RMSElog	d1.25	d1.252	d1.253
DDAD	Metric	0.221	4.001	13.406	0.340	0.688	0.868	0.932
	Median	0.221	3.884	13.225	0.328	0.692	0.877	0.939
NuScenes	Metric	0.285	6.662	7.472	0.347	0.741	0.883	0.936
	Median	0.258	4.282	7.226	0.329	0.735	0.883	0.937

Get Started

Training

Surround-view fusion depth estimation model can be trained from scratch.

• By default results are saved under results/<config-name> with trained model and tensorboard file for both training and validation.

Single-GPU
Training the model using single-GPU:
(Note that, due to usage of packnet-sfm submodule, userwarning repetitively occurs and therefore ignored while training.)

python -W ignore train.py --config_file='./configs/ddad/ddad_surround_fusion.yaml'
python -W ignore train.py --config_file='./configs/nuscenes/nusc_surround_fusion.yaml'

Multi-GPU
Training the model using Multi-GPU:

• Enable distributed data parallel(DDP), by setting ddp:ddp_enable to True in the config file
  • Gpus and the worldsize(number of gpus) must be specified (ex. gpus = [0, 1, 2, 3], worldsize= 4)
• DDP address and port setting can be configured in ddp.py

python -W ignore train.py --config_file='./configs/ddad/ddad_surround_fusion_ddp.yaml' 
python -W ignore train.py --config_file='./configs/nuscenes/nusc_surround_fusion_ddp.yaml'

Evaluation

To evaluate the trained model from scratch, run:

python -W ignore eval.py --config_file='./configs/<config-name>'

• The model weights need to be specified in load: weights of the config file.

Evaluation results using the pretrained model can be obtained by using the following command:

python -W ignore eval.py --config_file='./configs/<config-name>' \
                         --weight_path='<pretrained-weight-path>'

Depth Synthesis

To obtain synthesized depth results, train the model from scratch by running:

python -W ignore train.py --config_file='./configs/ddad/ddad_surround_fusion_augdepth.yaml'

Then evaluate the model by running:

python -W ignore eval.py --config_file='./configs/ddad/ddad_surround_fusion_augdepth.yaml'

• The synthesized results are stored results/<config-name>/syn_results

License

This repository is released under the Apach 2.0 license.