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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms
Authors: Zhihui Zhu, Yifan Wang, Daniel Robinson, Daniel Naiman, René Vidal, Manolis Tsakiris
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on road plane detection from 3D point cloud data demonstrate that DPCP-PSGM can be more efficient than the traditional RANSAC algorithm, which is one of the most popular methods for such computer vision applications. |
| Researcher Affiliation | Academia | Zhihui Zhu MINDS Johns Hopkins University EMAIL Yifan Wang SIST Shanghai Tech University EMAIL Daniel Robinson AMS Johns Hopkins University EMAIL Daniel Naiman AMS Johns Hopkins University EMAIL Rene Vidal MINDS Johns Hopkins University EMAIL Manolis C. Tsakiris SIST Shanghai Tech University EMAIL |
| Pseudocode | Yes | Algorithm 1 (DPCP-PSGM) Projected Sub-gradient Method for Solving (2) Input: data e X RD L and initial step size µ0; Initialization: set bb0 = arg minb e X b 2, s. t. b SD 1; 1: for k = 1, 2, . . . do 2: update the step size µk according to a certain rule; 3: bk = bbk 1 µk e X sign( e X bbk 1); bbk = PSD 1 (bk) = bk/ bk ; 4: end for |
| Open Source Code | No | No explicit statement providing access to the source code for the methodology described in this paper was found. No specific repository link was provided. |
| Open Datasets | Yes | Experiments on road plane detection from 3D point cloud data using the KITTI dataset [6], which is an important computer vision task in autonomous car driving systems |
| Dataset Splits | No | The paper states it manually annotated a few frames from the KITTI dataset, but does not provide specific details on train/validation/test splits (percentages, sample counts, or citations to predefined splits) needed for reproducibility. |
| Hardware Specification | Yes | Since DPCP-PSGM is the fastest method (on average converging in about 100 milliseconds for each frame on a 6 core 6 thread Intel (R) i5-8400 machine) |
| Software Dependencies | No | The paper mentions 'Gurobi [8]' as an efficient LP solver but does not provide a specific version number. No other key software components are listed with version numbers. |
| Experiment Setup | Yes | We set K0 = 30, K = 4 and β = 1/2 for the PGD step size with initial step size obtained by one iteration of a backtracking line search and denote the corresponding algorithm by PSGM-PGD. We define bb0 to be the bottom eigenvector of e X e X , which has been demonstrated to be effective in practice [24]. |