Harshil Bhatia1,2, Edith Tretschk2, Zorah Lähner3, Marcel Seelbach Benkner3, Michael Möller3, Christian Theobalt2 and Vladislav Golyanik2
1 Indian Institute of Technology, Jodhpur , 2Max Planck Institute for Informatics, 3 University of Siegen.
Arxiv | CVF | Code | Project Page
The code can be executed directly on D-Wave's Leap IDE or locally by installing D-Wave Ocean SDK
We recommend the user to create a virtual environment (optionally using conda)
conda env create -n ocean
pip install dwave-ocean-sdk
pip install pyqubo
Optionally, real quantum hardware can be used. First, we need to configure access to the Leap's Solvers. To achieve this, the user needs to create an account to gain access (a free account provides upto 1 minute of QPU time) to the solvers and retrieve their respective API tokens. Now run,
dwave setup
For more information related to the configuration please refer to this guide
Once the environment has been configured, we can run a 10 shape instance of the FAUST dataset on the first class using the following command
python Main.py --dataset FAUST --classvalue 0 --num_shapes 10
To run the same instance on the Quantum Annealer:
python Main.py --dataset FAUST --classvalue 0 --num_shapes 10 --nrWorst 20 --qpu --num_reads 200
Note: This repository contains precomputed geodesics and point descriptors for a subsampled version of the FAUST Dataset with one-to-one correspondences.
We run our experiments on instances of FAUST, TOSCA and SMAL. To run on custom data, configure each mesh in the following manner:
FAUSTLR
├───tr_reg_lr_000.mat
| ├─── S
| | ├─── TRIV
| | ├─── VERT
| ├─── geodesics
| ├─── descriptors
└─── ...
The dataset is organized into individual .mat files, each containing a single mesh model.
S/TRIV: triangle vertices of the mesh
S/VERT: vertex positions of the mesh
geodesics: pre-computed geodesic distance field for each vertex of the mesh
descriptors: pre-computed heat-kernel descriptors for each vertex of the mesh
Note: S/TRIV and S/VERT fields are not used by our algorithm, but are needed for visualisation purposes and are used to pre-compute the geodesics and descriptors.
If you find our work useful in your research, please consider citing:
@inproceedings{bhatia2022CCuantuMM,
title = {CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes},
author = {Bhatia, Harshil and Tretschk, Edith and Lähner, Zorah and Benkner, Marcel and Möller, Michael and Theobalt, Christian and Golyanik, Vladislav },
year = {2023},
booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
keywords = {Shape Analysis, Geometry Processing, Shape Correspondence, Multi Shape Matching},
}
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