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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