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..
Stereo Risk: A Continuous Modeling Approach to Stereo Matching
Authors: Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Yao Yao, Luc Van Gool
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comprehensive analysis demonstrates our method s theoretical soundness and superior performance over the state-of-the-art methods across various benchmark datasets, including KITTI 2012, KITTI 2015, ETH3D, Scene Flow, and Middlebury 2014. |
| Researcher Affiliation | Academia | 1Nanjing University, China. 2ETH Z urich, Switzerland. 3VCCM, Texas A&M University, USA. 4UESTC, China. 5University of W urzburg, Germany. 6KU Leuven, Belgium. 7INSAIT, Bulgaria. |
| Pseudocode | Yes | Algorithm 1 Forward Prediction input τ > 0, σ > 0, d = [d1, ..., d N], d1 < d2 < < d N, and pm = [pm 1 , ..., pm N] dl d1 dr d N g τ + 1 while |g| > τ do dm (dl + dr)/2.0 g P i pm i Sign(dm di)(1 exp |dm di| σ ) if g > 0 then end if end while output dm |
| Open Source Code | No | The paper does not explicitly state that its source code is open-source or provide a link to a code repository. |
| Open Datasets | Yes | Datasets. We perform experiments on four datasets namely Scene Flow (Mayer et al., 2016), KITTI 2012 & 2015 (Geiger et al., 2012; Menze & Geiger, 2015), Middlebury 2014 (Scharstein & Szeliski, 2002), and ETH 3D (Sch ops et al., 2017). |
| Dataset Splits | No | The paper specifies training and test splits (e.g., 'Scene Flow is a synthetic dataset containing 35,454 image pairs for training, and 4,370 image pairs for test.') but does not explicitly provide details for a separate validation split. |
| Hardware Specification | Yes | The software is evaluated on a machine with Ge Force-RTX-3090 GPU. |
| Software Dependencies | Yes | We implement our method in Py Torch 2.0.1 (Python 3.11.2) with CUDA 11.8. |
| Experiment Setup | Yes | We use Adam W optimizer (Loshchilov & Hutter, 2019) with weight decay 10 5. The learning rate decreases from 2 10 4 to 2 10 8 according to the one cycle learning rate policy. We train the network for 2 105 iterations. The images are randomly cropped to 320 736. |