Planar Prior Assisted PatchMatch Multi-View Stereo
Authors: Qingshan Xu, Wenbing Tao12516-12523
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our method can efficiently recover the depth information of extremely low-textured areas, thus obtaining high complete 3D models and achieving state-of-the-art performance. |
| Researcher Affiliation | Academia | National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artifical Intelligence and Automation, Huazhong University of Science and Technology, China {qingshanxu, wenbingtao}@hust.edu.cn |
| Pseudocode | Yes | The overall pipeline of our algorithm is summarized in Algorithm 1. |
| Open Source Code | Yes | Our code will be available at https://github.com/Ghi Xu/ACMP. |
| Open Datasets | Yes | We evaluate the effectiveness of our method on high-resolution multi-view stereo dataset of ETH3D benchmark (Sch ops et al. 2017). |
| Dataset Splits | Yes | The dataset is further split into training datasets and test datasets. |
| Hardware Specification | Yes | Our methods are implemented in C++ with CUDA and executed on a machine with two Intel E5-2630 CPUs and two GTX Titan X GPUs. |
| Software Dependencies | No | The paper mentions 'implemented in C++ with CUDA' but does not provide specific version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | {ϵ, α, γ, λn, σ, η, λgeo, τgeo} = {0.1, 0.18, 0.5, 5 , 0.3, 0.9, 0.1, 5.0}. Besides, λd is adaptively set to one sixty-fourth of the depth interval of every reference image. |