HOPE: Shape Matching Via Aligning Different K-hop Neighbourhoods
Authors: Barakeel Fanseu Kamhoua, Huamin Qu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experimental results that validate the effectiveness, efficiency, and generalization ability of HOPE in the matching of nearly-isometric and non-isometric 3D shapes. |
| Researcher Affiliation | Academia | Barakeel Fanseu Kamhoua1, Huamin Qu1 , 1The Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 : HOPE |
| Open Source Code | Yes | Justification: See Section 5 and attached code. |
| Open Datasets | Yes | We evaluate the performance of HOPE on two nearly isometric benchmark datasets TOSCA [7], and SCAPE [3], as well as on the non-isometric dataset SHREC 16 (TOPKIDS) [26], TOPKIDS |
| Dataset Splits | No | The paper discusses evaluation on "test pairs" and mentions using "all datasets" for parameter settings, but does not explicitly specify separate training, validation, and test dataset splits by name, or their sizes/percentages. |
| Hardware Specification | Yes | All experiments are conducted in Matlab 2023 on a Windows 11 system with 32GB RAM and Intel(R) i5 13500 CPU @ 2.50-4.8GHz. |
| Software Dependencies | No | The paper mentions 'Matlab 2023' but does not list other software dependencies with specific version numbers (e.g., libraries, frameworks). |
| Experiment Setup | Yes | On HOPE, on all datasets we set the LMD threshold ϵ, staring from ϵ = 100 and 10 equally spaced values to ϵ = 0.2 i.e., we use ϵ = linespace(100, 0.2, 10) and we set t = 60. When the last value of e is reached, it is maintained for the rest of the iterations. We equaly set kmax = 8 for all datasets. |