SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidance
Authors: Hongyu Yan, Zijun Li, Kunming Luo, Li Lu, Ping Tan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Qualitative and quantitative evaluations on several benchmark datasets demonstrate that our method outperforms state-of-the-art completion networks. |
| Researcher Affiliation | Academia | Hongyu Yan1*, Zijun Li2*, Kunming Luo1, Li Lu2 , Ping Tan1 1Hong Kong University of Science and Technology 2Sichuan University EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper describes the Local Symmetry Transformation Network (LSTNet) and Symmetry-Guidance Transformer (SGFormer) through descriptive text and architectural diagrams (Figures 1, 3, 4) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/Hongyu Yann/Symm Completion.git |
| Open Datasets | Yes | In our experiment, we use three wildly adopted synthetic datasets for training and evaluation, including the PCN dataset (Yuan et al. 2018), MVP dataset (Pan et al. 2021), and Shape Net55/34 dataset (Yu et al. 2021). Additionally, we test our method on the KITTI (Geiger et al. 2013) dataset to evaluate the network s generalization ability in real-world scenarios. |
| Dataset Splits | Yes | In our experiment, we use three wildly adopted synthetic datasets for training and evaluation, including the PCN dataset (Yuan et al. 2018), MVP dataset (Pan et al. 2021), and Shape Net55/34 dataset (Yu et al. 2021). Additionally, we test our method on the KITTI (Geiger et al. 2013) dataset to evaluate the network s generalization ability in real-world scenarios. Following previous methods (Yu et al. 2021; Zhu et al. 2023), we study the generalization capability of Symm Completion on the 34 seen categories and 21 unseen categories. |
| Hardware Specification | No | The paper does not explicitly provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | No | The paper does not explicitly detail specific experimental setup parameters such as hyperparameters (learning rate, batch size, number of epochs), optimizer settings, or training schedules in the main text. |