MeshNet: Mesh Neural Network for 3D Shape Representation
Authors: Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, Yue Gao8279-8286
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed Mesh Net can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation. |
| Researcher Affiliation | Academia | 1BNRist, KLISS, School of Software, Tsinghua University, China. 2School of Information Science and Engineering, Xiamen University |
| Pseudocode | No | The paper describes the architecture and components of Mesh Net using text and diagrams (e.g., Figure 2) but does not provide formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We apply our network on the Model Net40 dataset (Wu et al. 2015) for classification and retrieval tasks. |
| Dataset Splits | Yes | The dataset contains 12,311 mesh models from 40 categories, in which 9,843 models for training and 2,468 models for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types) used for running the experiments. It only discusses time and space complexity generally. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | Yes | We use the SGD optimizer for training, with initial learning rate 0.01, momentum 0.9, weight decay 0.0005 and batch size 64. |