Hypernetwork approach to generating point clouds
Authors: Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zieba, Tomasz Trzcinski
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we describe the experimental results of the proposed generative models in various tasks, including 3D points mesh generation and interpolation. |
| Researcher Affiliation | Collaboration | 1Faculty of Mathematics and Computer Science, Jagiellonian University, Krak ow, Poland 2Wrocław University of Science and Technology, Wrocław, Poland 3Tooploox, Wrocław, Poland 4Warsaw University of Technology, Warsaw, Poland. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1We make our implementation available at https:// github.com/gmum/3d-point-clouds-Hyper Cloud |
| Open Datasets | Yes | We train each model using point clouds from one of the three categories in the Shape Net dataset: airplane, chair, and car. |
| Dataset Splits | No | The paper states 'We follow the exact evaluation pipeline provided in (Yang et al., 2019)', implying the use of established splits, but it does not explicitly provide specific percentages, sample counts, or detailed methodology for dataset splitting. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as library or solver names with their respective versions. |
| Experiment Setup | No | The paper does not contain specific experimental setup details like concrete hyperparameter values (e.g., learning rate, batch size, epochs) or detailed training configurations in the main text. |