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..
Joint Multi-view 2D Convolutional Neural Networks for 3D Object Classification
Authors: Jinglin Xu, Xiangsen Zhang, Wenbin Li, Xinwang Liu, Junwei Han
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the proposed method is able to outperform current state-of-the-art methods on 3D object classification. |
| Researcher Affiliation | Academia | 1Northwestern Polytechnical University, Xi an, China 2Nanjing University, Nanjing, China 3National University of Defense Technology, Changsha, China |
| Pseudocode | No | The paper does not contain a pseudocode block or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology. |
| Open Datasets | Yes | Model Net40 [Wu et al., 2015] provided on the Princeton Model Net website 1 is a subset of the Model Net and has 12311 models from 40 common categories. 1http://modelnet.cs.princeton.edu/ |
| Dataset Splits | Yes | For the classification task, all the works are discussed on the Model Net40, referring to [Su et al., 2015] to conduct the training/testing split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory specifications) to run its experiments. |
| Software Dependencies | No | The paper mentions software components like "Res Net-18" and "Adam" but does not specify version numbers for programming languages or libraries. |
| Experiment Setup | Yes | For our proposed method, we fine-tune the parameters of Res Net-18 using the Model Net40 dataset and use Adam with learning rate=5 10 6, β1 =0.9, β2 =0.999, weight decay= 0.001, batch size = 8, epoch = 30 for optimization. Furthermore, there are two parameters s and γ in the proposed method, where s denotes the number of nonzero elements in α and γ is the power exponent of each element of α. For one thing, we tune s in the range of [6, 12] with step 1 to select a few discriminative and informative views to make a joint decision during classification. For another thing, we vary γ from 1.5 to 10 with a step of 1 to explore the influence on different values of γ on classification accuracy. |