Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Planar Prior Assisted PatchMatch Multi-View Stereo
Authors: Qingshan Xu, Wenbing Tao12516-12523
AAAI 2020 | Venue PDF | 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 EMAIL |
| 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. |