Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization
Authors: Jiayi Ma, Ji Zhao, Junjun Jiang, Huabing Zhou
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both 2D and 3D data demonstrate that our method can yield superior results compared to other state-ofthe-arts, especially in case of badly degraded data. |
| Researcher Affiliation | Academia | 1Electronic Information School, Wuhan University, Wuhan 430072, China 2School of Computer Science, China University of Geosciences, Wuhan 430074, China 3Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430073, China |
| Pseudocode | Yes | Algorithm 1: The MR-RPM Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use the same synthesized data as that in (Chui and Rangarajan 2003) and (Zheng and Doermann 2006), which consists of two shape patterns (i.e., a fish and a Chinese character) with different kinds of degenerations including deformation, noise, outlier, rotation and occlusion. |
| Dataset Splits | No | The paper describes that each degeneration level contains 100 samples for evaluation, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or absolute counts). |
| Hardware Specification | Yes | The experiments were performed on a laptop with 3.0 GHz Intel Core CPU, 8 GB memory and Matlab Code. |
| Software Dependencies | No | The paper mentions 'Matlab Code' but does not specify a version number for Matlab or any other software dependencies with their versions. |
| Experiment Setup | Yes | We set ϵ = 0.05, β = 0.1, λ1 = 3, λ2 = 0.05 throughout this paper. The inlier percentage parameter γ needs an initial assumption, as shown in Line 8 in Algorithm 1, here we fix it to 0.9. Moreover, to use the fast implementation, we set the solution base number K to 15 in the 2D case and 50 in the 3D case; the uniform distribution parameter a is set to be the volume of the bounding box of the data. |