Authors:
- Alessio Xompero
- Ricardo Sanchez-Matilla
- Apostolos Modas
- Pascal Frossard
- Andrea Cavallaro
Created date: 2020/02/28
Version: 0.1
Resource type: software
LoDE (Localisation and Object Dimensions Estimator) is a method for jointly localising container-like objects and estimating their dimensions using two wide-baseline, calibrated RGB cameras. Under the assumption of circular symmetry along the vertical axis, LoDE estimates the dimensions of an object with a generative 3D sampling model of sparse circumferences, iterative shape fitting and image re-projection to verify the sampling hypotheses in each camera using semantic segmentation masks (Mask R-CNN).
LoDE webpage CORSMAL Containers dataset
- Python 3.6.8
- OpenCV 4.1.0
- PyTorch 1.4.0
- TorchVision 0.5.0
- NVIDIA CUDA 10.1
- CORSMAL Containers dataset
Tested on Linux machine with Ubuntu 16.04 LTS
Download or clone the repository.
git clone https://github.com/CORSMAL/LoDE.git
We recomend creating an anaconda environment (more info on how to install miniconda)
conda create -n LoDE python=3.6.8
source activate LoDE
Install dependencies in the environment
pip install -r requirements.txt
Download the CORSMAL Containers dataset
cd <rootPath>
wget http://corsmal.eecs.qmul.ac.uk/data/ICASSP20/CORSMAL_containers_dataset.zip
unzip CORSMAL_containers_dataset.zip
mv CORSMAL_Containers dataset
rm CORSMAL_containers_dataset.zip
The dataset should be in the same working directory than LoDE. The dataset folder should be named dataset and should be structured as the CORSMAL Containers dataset (see current structure).
Run LoDE on the whole dataset
python main.py --object=0 --draw
Run LoDE with a sample of the CORSMAL Containers dataset (e.g. object 15, lighting 0, and background 0; contained on ./dataset/images)
python main.py --object=15 --lighting=0 --background=0 --draw
LoDE outputs two results:
- Dimensions estimation of the height and width of the container in milimeters in results/estimation.txt
- Visual representation of the container shape in results/*.png. The visual representation can be removed by omitting the --draw commands
If you use this data, please cite: A. Xompero, R. Sanchez-Matilla, A. Modas, P. Frossard, and A. Cavallaro, Multi-view shape estimation of transparent containers, Published in the IEEE 2020 International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain, 4-8 May 2020.
Bibtex: @InProceedings{Xompero2020ICASSP, TITLE = {Multi-view shape estimation of transparent containers}, AUTHOR = {A. Xompero, R. Sanchez-Matilla, A. Modas, P. Frossard, and A. Cavallaro}, BOOKTITLE = {IEEE 2020 International Conference on Acoustics, Speech, and Signal Processing}, ADDRESS = {Barcelona, Spain}, MONTH = "4--8~" # MAY, YEAR = 2020 }
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