This repository contains the source code for our paper:
Elevation Angle Estimation in 2D Acoustic Images Using Pseudo Front View
RAL and ICRA2021
pytorch 1.2.0
torchvision 0.40
tensorboardX
python train.py --train_path to_your_train_label --test_path to_your_validate_label
During training, using front depth map instead of inverse front depth map can achieve better results
The simulation dataset used in this paper can be download from the following links.
water tank
floating object
The structure of the dataset is as follows.
├── water-tank
│ ├── bricks
├── sfront.txt //inverse front depth map without quantization loss
├── resizeimg.png // synthetic acoustic image
├── ele_resize.png // ground truth of elevation angle
├── front_resize.png // inverse front depth map for visualization only
│ └── cylinder
For the simulator used to generate synthetic datasets, check here.
How to evaluate depth map is shown in eval.ipynb.