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..
WT-MVSNet: Window-based Transformers for Multi-view Stereo
Authors: Jinli Liao, Yikang Ding, Yoli Shavit, Dihe Huang, Shihao Ren, Jia Guo, Wensen Feng, Kai Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our WT-MVSNet method (WTMVSNet) achieves state-of-the-art performance across multiple datasets and ranks 1st on Tanks and Temples benchmark. Extensive experiments show that our method achieves state-of-the-art performance on multiple datasets. |
| Researcher Affiliation | Collaboration | Jinli Liao 1,2 Yikang Ding 1 Yoli Shavit 3 Dihe Huang 1 Shihao Ren 1,2 Jia Guo 2 Wensen Feng 2 Kai Zhang 1,4 1 Tsinghua University 2 Huawei Technologies 3 Bar-Ilan University 4 Research Institute of Tsinghua, Pearl River Delta |
| Pseudocode | No | The paper does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] In the supplemental material. |
| Open Datasets | Yes | We implement WT-MVSNet based on Pytorch, which is trained on DTU training set. Section 4.2 'Datasets' describes 'DTU is an indoor dataset...', 'Tanks and Temples is a large-scale benchmark...', 'Blended MVS is a large-scale synthetic dataset...'. All are well-established and cited benchmarks. |
| Dataset Splits | Yes | DTU dataset is split into 79 training scans, 18 validation scans, and 22 evaluation scans. |
| Hardware Specification | Yes | We train our model with the batch size being set to 1 on 8 Tesla V100 GPUs. |
| Software Dependencies | No | We implement WT-MVSNet based on Pytorch. The paper does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | We train our model using Adam for 16 epochs at a learning rate of 0.001, which decays by a factor of 0.5 after 6, 8, 12 epochs, respectively. We set combination coefficient γ = 100.0, the loss weights λ1 = 2.0 and λ2 = 1.0, the reprojection errors thresholds τ1 to 3.0, 2.0, 1.0 and τ2 to 0.1, 0.05, 0.01 at 3 resolutions. We train our model with the batch size being set to 1 on 8 Tesla V100 GPUs. |