Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching
Authors: Kelvin Cheng, Tianfu Wu, Christopher Healey
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, the proposed method is tested in the Scene Flow dataset, the KITTI2015 dataset, and the Middlebury dataset. It significantly improves the adversarial robustness, while retaining accuracy performance comparable to state-of-the-art methods. |
| Researcher Affiliation | Academia | Kelvin Cheng , Tianfu Wu and Christopher Healey CS and ECE at NC State University, Raleigh, NC 27695 {kbcheng, twu19, healey}@ncsu.edu |
| Pseudocode | No | The paper does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and pretrained models are released at this Github Repo. |
| Open Datasets | Yes | We evaluate our method on the Scene Flow [5] and KITTI2015 [6] datasets. We also test pretrained models on the KITTI2012 [15] and the Middlebury [7] dataset at quarter resolution. |
| Dataset Splits | Yes | The Scene Flow dataset is a large-scale synthetic dataset that contains 35, 454 training images and 4, 370 test images at the resolution of 540 × 960. The KITTI2015 dataset is a real-world dataset of driving scenes, which contains 200 training images and 200 test images at the resolution of 375 × 1242. |
| Hardware Specification | No | The main text of the paper does not specify any particular GPU, CPU, or other hardware details used for running experiments. Appendix D is referenced for compute details, but not provided in the given text. |
| Software Dependencies | No | The paper mentions software components like 'smooth L1 loss' and 'sigmoid function', but does not provide specific version numbers for any software libraries or frameworks used. |
| Experiment Setup | Yes | In our experiments, we set = 0.06 or 0.03... = 0.01 and T = 20. We use local windows with sizes from k1 to k2 (e.g. k1 = 3, k2 = 11 in our experiments)... (e.g., 0.5, 0.7, and 1 are used for the 3-stack Hourglass module in our experiments). |