Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Dual-Resolution Correspondence Networks
Authors: Xinghui Li, Kai Han, Shuda Li, Victor Prisacariu
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,909 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,414 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We comprehensively evaluate our method on large-scale public benchmarks including HPatches, In Loc, and Aachen Day-Night. It achieves the state-of-the-art results on all of them. |
| Researcher Affiliation | Academia | 1Active Vision Lab, University of Oxford 1EMAIL 2Visual Geometry Group, University of Oxford EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code can be found at https://code.active.vision. |
| Open Datasets | Yes | We train our models on Mega Depth dataset [50], which consists of a large number of internet images about 196 scenes and their sparse 3D point clouds are constructed by COLMAP [51, 52]. |
| Dataset Splits | Yes | We use the scenes with more than 500 valid image pairs for training and the rest scenes for validation. To avoid scene bias, 110 image pairs are randomly selected from each training scene to constitute our training set. In total, we obtain 15, 070 training pairs and 14, 638 validation pairs. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions implementing the pipeline in Pytorch [48], but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We train our model using Adam optimizer [49] for 15 epochs with an initial learning rate of 0.01 which is halved every 5 epochs. |