Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms
Authors: Zhihui Zhu, Yifan Wang, Daniel Robinson, Daniel Naiman, René Vidal, Manolis Tsakiris
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 zzhu29@jhu.edu Yifan Wang SIST Shanghai Tech University wangyf@shanghaitech.edu.cn Daniel Robinson AMS Johns Hopkins University daniel.p.robinson@jhu.edu Daniel Naiman AMS Johns Hopkins University daniel.naiman@jhu.edu Rene Vidal MINDS Johns Hopkins University rvidal@jhu.edu Manolis C. Tsakiris SIST Shanghai Tech University mtsakiris@shanghaitech.edu.cn |
| 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]. |