Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
Authors: Ramana Subramanyam Sundararaman, Riccardo Marin, Emanuele Rodolà, Maks Ovsjanikov
NeurIPS 2022 | Venue PDF | 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. |