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..
Any-Stereo: Arbitrary Scale Disparity Estimation for Iterative Stereo Matching
Authors: Zhaohuai Liang, Changhe Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments Datasets & Evaluation Metrics Datasets The Secene Flow dataset (Mayer et al. 2016), KITTI dataset (Geiger, Lenz, and Urtasun 2012; Menze and Geiger 2015) and Middlebury dataset (Scharstein et al. 2014) are used. [...] Table 1: Quantitative evaluation on KITTI 2015 and KITTI 2012. |
| Researcher Affiliation | Academia | Zhaohuai Liang 1, Changhe Li 2* 1 School of Automation, China University of Geosciences, Wuhan 430074, China 2 School of Artificial Intelligence, Anhui University of Science & Technology, Hefei 232001, China |
| Pseudocode | No | The paper describes the proposed methods and their components in detail but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/Zhaohuai-L/Any-Stereo. |
| Open Datasets | Yes | Datasets The Secene Flow dataset (Mayer et al. 2016), KITTI dataset (Geiger, Lenz, and Urtasun 2012; Menze and Geiger 2015) and Middlebury dataset (Scharstein et al. 2014) are used. |
| Dataset Splits | Yes | KITTI12 consists of 194 training pairs and 195 testing pairs, and KITTI15 consists of 200 training pairs and 200 testing pairs. [...] All models are trained on Scene Flow training sets, and evaluated on the testing sets with a fixed scale to full resolution. |
| Hardware Specification | Yes | we perform our experiments on two NVIDIA A40 GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | For all training, we use the Adam W (Kingma and Ba 2014) optimizer with a one-cycle learning rate schedule and clip gradients to [-1,1]. For the ISU, we set window size p as 5. [...] The model for ablation is trained on Scene Flow for 100k steps with a batch size 6. The final model is pretrained on Scene Flow for 200k steps with a batch size of 8, and then finetuned on KITTI and Middlebury. All experiments are run with 22 update iterations during training, 32 during evaluation. |