Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
Authors: Ramana Subramanyam Sundararaman, Riccardo Marin, Emanuele Rodolà, Maks Ovsjanikov
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate its effectiveness through stateof-the-art results across multiple deformable shape matching benchmarks. Our code and data are publicly available at: https://github.com/Sentient07/ Deformation Basis. |
| Researcher Affiliation | Academia | Ramana Sundararaman1, Riccardo Marin2,3, Emanuele Rodolà3, and Maks Ovsjanikov1 1LIX, Ecole Polytechnique, IP Paris 2University of Tübingen 3Sapienza University of Rome |
| Pseudocode | No | No structured pseudocode or algorithm blocks are provided. The method is described using text and mathematical equations. |
| Open Source Code | Yes | Our code and data are publicly available at: https://github.com/Sentient07/ Deformation Basis. |
| Open Datasets | Yes | Our code and data are publicly available at: https://github.com/Sentient07/ Deformation Basis. |
| Dataset Splits | Yes | We train our method on a subset of 1000 SURREAL shapes [80] for 1000 epochs with data-augmentation along Y-axis. |
| Hardware Specification | No | The main paper does not provide specific hardware details (GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. The ethics checklist mentions details are in the supplementary, but the prompt asks for main paper body. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | We train our method on a subset of 1000 SURREAL shapes [80] for 1000 epochs with data-augmentation along Y-axis. |