CDPNet: Cross-Modal Dual Phases Network for Point Cloud Completion
Authors: Zhenjiang Du, Jiale Dou, Zhitao Liu , Jiwei Wei, Guan Wang, Ning Xie, Yang Yang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our method achieves state-of-the-art performance on point cloud completion. |
| Researcher Affiliation | Academia | 1University of Electronic Science and Technology of China, Chengdu, China 2 Yibin Park, University of Electronic Science and Technology of China, Yibin, China 3Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China |
| Pseudocode | No | The paper describes the model architecture and components in text and diagrams (Figure 2 and Figure 3), but does not provide pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | The dataset we used in our experiments is Shape Net Vi PC (Zhang et al. 2021). |
| Dataset Splits | No | The paper states: 'for each category, we randomly take about 1/3 of the total training data for training. For the test dataset, we used all the test data.' It does not explicitly mention a validation split with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | We train the network with a batch size of 32. The initial learning rate is 1e-4 and decayed by 0.7 after 20 epochs. The optimization is set to stop after 150 epochs. The initial value of α is set to 1, which will change with the number of iterations. After iterating 50 epochs, we set it to 0.5. The method s hyper-parameters (η, λ, ξ) are set to: (0.1, 0.1, 0.01). |