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..
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 | Venue PDF | 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 Ξ± 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 (Ξ·, Ξ», ΞΎ) are set to: (0.1, 0.1, 0.01). |