ReSync: Riemannian Subgradient-based Robust Rotation Synchronization
Authors: Huikang Liu, Xiao Li, Anthony Man-Cho So
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results demonstrate the effectiveness of Re Sync. |
| Researcher Affiliation | Academia | Huikang Liu School of Information Management and Engineering Shanghai University of Finance and Economics liuhuikang@shufe.edu.cn; Xiao Li School of Data Science The Chinese University of Hong Kong, Shenzhen lixiao@cuhk.edu.cn; Anthony Man-Cho So Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong manchoso@se.cuhk.edu.hk |
| Pseudocode | Yes | Algorithm 1 Re Sync: Riemannian Subgradient Synchronization; Algorithm 2 Spectr In: Spectral Initialization |
| Open Source Code | Yes | Our code is available at https://github.com/Huikang2019/Re Sync. |
| Open Datasets | Yes | We consider the global alignment problem of three-dimensional scans from the Lucy dataset, which is a down-sampled version of the dataset containing 368 scans with a total number of 3.5 million triangles. We refer to [39] for more details about the experiment setting. |
| Dataset Splits | No | The paper describes data generation for synthetic data and the use of the Lucy dataset, but does not specify explicit train/validation/test splits (e.g., percentages, sample counts, or predefined splits) for reproducibility. |
| Hardware Specification | Yes | Our experiments are conducted on a personal computer with a 2.90GHz 8-core CPU and 32GB memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for its implementation or experiments. |
| Experiment Setup | Yes | We use the initial step size µ0 = 1/npq and the decaying factor γ {0.7, 0.8, 0.85, 0.90, 0.95, 0.98} in Re Sync. |