Primal-Dual Mesh Convolutional Neural Networks
Authors: Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate PD-Mesh Net on the tasks of mesh classification and mesh segmentation. On these tasks and on several datasets, we outperform start-of-the-art methods. |
| Researcher Affiliation | Academia | Francesco Milano ETH Zurich, Switzerland fmilano@student.ethz.ch Antonio Loquercio Robotics and Perception Group University of Zurich, Switzerland loquercio@ifi.uzh.ch Antoni Rosinol SPARK Lab MIT, USA arosinol@mit.edu Davide Scaramuzza Robotics and Perception Group University of Zurich, Switzerland sdavide@ifi.uzh.ch Luca Carlone SPARK Lab MIT, USA lcarlone@mit.edu |
| Pseudocode | No | The paper describes its methods through prose and diagrams but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/MIT-SPARK/PD-Mesh Net. |
| Open Datasets | Yes | SHREC dataset [48], Cube Engraving dataset released by [12], COSEG [56] and Human Body [57]. |
| Dataset Splits | No | The paper provides detailed training and testing splits for the datasets but does not explicitly mention a separate validation split. For example, for the SHREC dataset: “split 16 where for each class 16 samples are used for training and 4 for testing and split 10 in which the samples of each class are subdivided equally between training and the test set.” and for Cube Engraving: “(170 training samples and 30 test samples)”. No explicit mention of validation data for hyperparameter tuning. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running its experiments (e.g., CPU/GPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper mentions software like “Py Torch [46]” and “Py Torch Geometric [47]” but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | In all the experiments we use Adam algorithm [45] for optimization. The network is trained using cross-entropy on the predicted labels. ... We limit the number of training epochs to 200... Every node of the resulting primal graph...is trained using cross-entropy loss for 1000 epochs. ...we generate 20 augmented versions of each training sample by randomly shifting the vertices along the edges. |