Lite-Mono

A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation [paper link]

Ning Zhang*, Francesco Nex, George Vosselman, Norman Kerle

(Lite-Mono-8m 1024x320)

Table of Contents

• Overview
• Results
  • KITTI
  • Speed Evaluation
  • Robustness
• Data Preparation
• Single Image Test
• Evaluation
• Training
  • Dependency Installation
  • Start Training
  • Tensorboard Visualization
• Citation

Overview

Results

KITTI

You can download the trained models using the links below.

--model	Params	ImageNet Pretrained	Input size	Abs Rel	Sq Rel	RMSE	RMSE log	delta < 1.25	delta < 1.25^2	delta < 1.25^3
lite-mono	3.1M	yes	640x192	0.107	0.765	4.561	0.183	0.886	0.963	0.983
lite-mono-small	2.5M	yes	640x192	0.110	0.802	4.671	0.186	0.879	0.961	0.982
lite-mono-tiny	2.2M	yes	640x192	0.110	0.837	4.710	0.187	0.880	0.960	0.982
lite-mono-8m	8.7M	yes	640x192	0.101	0.729	4.454	0.178	0.897	0.965	0.983
lite-mono	3.1M	yes	1024x320	0.102	0.746	4.444	0.179	0.896	0.965	0.983
lite-mono-small	2.5M	yes	1024x320	0.103	0.757	4.449	0.180	0.894	0.964	0.983
lite-mono-tiny	2.2M	yes	1024x320	0.104	0.764	4.487	0.180	0.892	0.964	0.983
lite-mono-8m	8.7M	yes	1024x320	0.097	0.710	4.309	0.174	0.905	0.967	0.984

Speed Evaluation

Robustness

The RoboDepth Challenge Team is evaluating the robustness of different depth estimation algorithms. Lite-Mono has achieved the best robustness to date.

Data Preparation

Please refer to Monodepth2 to prepare your KITTI data.

Single Image Test

python test_simple.py --load_weights_folder path/to/your/weights/folder --image_path path/to/your/test/image

Evaluation

python evaluate_depth.py --load_weights_folder path/to/your/weights/folder --data_path path/to/kitti_data/ --model lite-mono

Training

dependency installation

pip install 'git+https://github.com/saadnaeem-dev/pytorch-linear-warmup-cosine-annealing-warm-restarts-weight-decay'

start training

python train.py --data_path path/to/your/data --model_name mytrain --num_epochs 30 --batch_size 12 --mypretrain path/to/your/pretrained/weights  --lr 0.0001 5e-6 31 0.0001 1e-5 31

tensorboard visualization

tensorboard --log_dir ./tmp/mytrain

Citation

@article{zhang2022lite,
title={Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation},
author={Zhang, Ning and Nex, Francesco and Vosselman, George and Kerle, Norman},
journal={arXiv preprint arXiv:2211.13202},
year={2022}
}