Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MeshNet: Mesh Neural Network for 3D Shape Representation
Authors: Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, Yue Gao8279-8286
AAAI 2019 | Venue PDF | 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. |