Adaptive Wavelet Transformer Network for 3D Shape Representation Learning
Authors: Hao Huang, Yi Fang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that AWT-Net achieves competitive performance on 3D shape classification and segmentation benchmarks. |
| Researcher Affiliation | Academia | Hao Huang, Yi Fang NYU Multimedia and Visual Computing Lab, USA NYUAD Center for Artificial Intelligence and Robotics (CAIR), Abu Dhabi, UAE NYU Tandon School of Engineering, New York University, USA New York University Abu Dhabi, UAE {hh1811,yfang}@nyu.edu |
| Pseudocode | Yes | Algorithm 1: Even-Odd Node Split |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate our model on 3D shape classification and part segmentation tasks on three datasets following the settings (Qi et al., 2017a;b; Wang et al., 2019; Xu et al., 2021b; Zhao et al., 2021): Model Net40 (Wu et al., 2015), Scan Object NN (Uy et al., 2019) and Shape Net Part (Yi et al., 2016). |
| Dataset Splits | Yes | We evaluate our model on 3D shape classification and part segmentation tasks on three datasets following the settings (Qi et al., 2017a;b; Wang et al., 2019; Xu et al., 2021b; Zhao et al., 2021): Model Net40 (Wu et al., 2015) which contains 9,843 training shapes and 2,468 test shapes across 40 categories. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU model, CPU type, memory) used to run its experiments. It only mentions 'The computational overhead is measured in second for a single forward and backward pass.' |
| Software Dependencies | No | We implement our model in Py Torch (Paszke et al., 2019). (No version number for PyTorch or any other software is provided.) |
| Experiment Setup | Yes | We use SGD optimizer with momentum and weight decay set to 0.9 and 0.0001, respectively. For both 3D shape classification and 3D object part segmentation tasks, we train for 350 epochs. The initial learning rate is set to 0.2 for shape classification and 0.005 for object part segmentation, and clipped at 0.9e 5. The values of λ1 and λ2 are set to 0.1 for all tasks. |