Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Hypergraph Optimization for Multi-Structural Geometric Model Fitting
Authors: Shuyuan Lin, Guobao Xiao, Yan Yan, David Suter, Hanzi Wang8730-8737
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,138 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,173 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers. |
| Researcher Affiliation | Academia | 1Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China 2Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, China 3School of Science, Edith Cowan University, Australia |
| Pseudocode | Yes | Algorithm 1: The adaptive inlier estimation (AIE) Algorithm 2: The iterative hyperedge optimization (IHO) Algorithm 3: The hypergraph optimization based model fitting (HOMF) method |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology. |
| Open Datasets | Yes | We evaluate the performance on 16 representative image pairs with single-structure and multiple-structural data from the Adelaide RMF datasets (Wong et al. 2011) |
| Dataset Splits | No | The paper describes testing on datasets but does not specify explicit training/validation/test splits or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Specifically, the sampling frequency to 200 times for the proposed method in the experiments... we set the q to be 0.1 n. Input: The initial hyperedge E(e), the vertices V = {vi}n i=1, the minimum tolerable size q, the higher than minimal subset l and the number of iterations Tmax. Input: A set of data points X = {xi}n i=1, the number of model hypothesis m and the number of structures c. |