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..
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 | 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. |