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..
SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM Optimization
Authors: Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang, Zhaoqi Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations on the ETH3D high-resolution multi-view stereo benchmark and the Tanks and Temples dataset demonstrate that our method can achieve state-of-the-art results with less time consumption. ... Experiments Datasets and Implementation Details We evaluate our work on both ETH3D high-resolution benchmark (Sch ops et al. 2017) and Tanks and Temples benchmark (TNT) (Knapitsch et al. 2017). |
| Researcher Affiliation | Academia | 1Institute of Computing Technology, Chinese Academy of Sciences 2Agricultural Information Institute, Chinese Academy of Agricultural Sciences 3Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The methods are described in prose and mathematical formulations. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate our work on both ETH3D high-resolution benchmark (Sch ops et al. 2017) and Tanks and Temples benchmark (TNT) (Knapitsch et al. 2017). |
| Dataset Splits | Yes | We evaluate our work on both ETH3D high-resolution benchmark (Sch ops et al. 2017) and Tanks and Temples benchmark (TNT) (Knapitsch et al. 2017). |
| Hardware Specification | Yes | Our method is implemented on a system equipped with an Intel(R) Core(TM) i7-10700 CPU @ 2.90GHz and an NVIDIA Ge Force RTX 3080 graphics card. |
| Software Dependencies | No | The paper mentions 'CUDA operators' and the 'Segment Anything Model (SAM)' but does not provide specific version numbers for any software dependencies, libraries, or frameworks used for reproducibility. |
| Experiment Setup | Yes | Concerning parameter setting, {wms, wrp, wpc, L, k, τ, Nmax, η} = {1, 0.2, 0.2, 11, 3, 2, 3, 0.1}. In cost calculation, we take the matching strategy of every other row and column. |