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..
A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding
Authors: Yitong Dong, Yijin Li, Zhaoyang Huang, Weikang Bian, Jingbo Liu, Hujun Bao, Zhaopeng Cui, Hongsheng Li, Guofeng Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results on the DTU dataset and Tanks&Temple benchmark demonstrate the effectiveness of our method. |
| Researcher Affiliation | Collaboration | 1State Key Lab of CAD&CG, Zhejiang University 2CUHK MMLab |
| Pseudocode | No | The paper describes the method using text and mathematical equations, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | We plan to release the code and detailed results later. |
| Open Datasets | Yes | DTU dataset [23] is an indoor multi-view stereo dataset... Blended MVS dataset [66] is a large-scale outdoor multi-view stereo dataset... Tanks and Temples [24] is a public multi-view stereo benchmark |
| Dataset Splits | Yes | Following MVSNet [8], we partitioned the DTU dataset into 79 training sets, 18 validation sets, and 22 evaluation sets. |
| Hardware Specification | Yes | The training procedure is finished on two A100 |
| Software Dependencies | No | The paper mentions 'Implemented by PyTorch [67]' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | On the DTU dataset, we set the image resolution as 640 512 and the number of input images as 5 for the training phase... For all models, we use the Adam W optimizer with an initial learning rate of 0.0002 that halves every four epochs for 16 epochs. |