Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Correspondence learning via linearly-invariant embedding
Authors: Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,405 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,967 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our pipeline on the correspondence problem between non-rigid 3D point clouds in the challenging class of human models. Finally, we demonstrate that our approach achieves state-of-the-art results in challenging non-rigid 3D point cloud correspondence applications. |
| Researcher Affiliation | Academia | Riccardo Marin University of Verona EMAIL Marie-Julie Rakotosaona LIX, Ecole Polytechnique, IP Paris EMAIL Simone Melzi LIX, Ecole Polytechnique, IP Paris Sapienza University of Rome EMAIL Maks Ovsjanikov LIX, Ecole Polytechnique, IP Paris EMAIL |
| Pseudocode | No | The paper describes the pipeline and methods in text and uses flow diagrams (Figure 1), but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | The code, datasets and our pre-trained networks can be found online: https://github.com/riccardomarin/Diff-FMaps. |
| Open Datasets | Yes | For our experiments we train over 10K shapes from the SURREAL dataset [57], resampled at 1K vertices. We consider a ο¬rst test set composed by the 100 shapes from the FAUST dataset [6] (10 different subjects in 10 different poses). [1] Scan the world project. https://www.myminifactory.com/scantheworld |
| Dataset Splits | No | The paper mentions training on the SURREAL dataset and testing on the FAUST dataset but does not provide specific train/validation/test split percentages or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper states that 'Both of our networks N and G are built upon the Point Net architecture [44]' but does not provide specific version numbers for any software dependencies, libraries, or programming languages used in its implementation. |
| Experiment Setup | Yes | Both of our networks N and G are built upon the Point Net architecture [44]. For our experiments we train over 10K shapes from the SURREAL dataset [57], resampled at 1K vertices. We learn a k = 20 dimensional embedding (basis) and p = 40 probe functions for each point cloud. |