Correspondence learning via linearly-invariant embedding
Authors: Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 riccardo.marin_01@univr.it Marie-Julie Rakotosaona LIX, Ecole Polytechnique, IP Paris mrakotos@lix.polytechnique.fr Simone Melzi LIX, Ecole Polytechnique, IP Paris Sapienza University of Rome melzi@di.uniroma1.it Maks Ovsjanikov LIX, Ecole Polytechnique, IP Paris maks@lix.polytechnique.fr |
| 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 first 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. |