Learning Local Neighboring Structure for Robust 3D Shape Representation
Authors: Zhongpai Gao, Junchi Yan, Guangtao Zhai, Juyong Zhang, Yiyan Yang, Xiaokang Yang1397-1405
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate that our model produces significant improvement in 3D shape reconstruction compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | Zhongpai Gao1, Junchi Yan1, Guangtao Zhai1,2 , Juyong Zhang3, Yiyan Yang1, Xiaokang Yang1 1 Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University 2 Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University 3 School of Mathematical Sciences, University of Science and Technology of China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code has been formally released at: https://github.com/Gaozhongpai/ Pai Conv Mesh. |
| Open Datasets | Yes | We evaluate our model on two datasets: COMA (Ranjan et al. 2018) and DFAUST (Bogo et al. 2017). |
| Dataset Splits | Yes | We split both two datasets into training and test set with a ratio of 9:1 and randomly select 100 samples from the training set for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using Adam optimizer and specific network architectures (e.g., ResNet-50, Point Net), but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We use Adam (Kingma and Ba 2014) optimizer with learning rate 0.001 and reduce the learning rate with decay rate 0.99 in every epoch. The batch size is 32 and total epoch number is 300. |