A Global Occlusion-Aware Approach to Self-Supervised Monocular Visual Odometry
Authors: Yao Lu, Xiaoli Xu, Mingyu Ding, Zhiwu Lu, Tao Xiang2260-2268
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the KITTI dataset show that our approach achieves new state-of-the-art in both pose estimation and depth recovery. |
| Researcher Affiliation | Academia | 1 Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China 2 Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing 100872, China 3 The University of Hong Kong, Pokfulam, Hong Kong, China 4 University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | For single-view depth estimation, we select the popular KITTI raw dataset (Geiger et al. 2013) with the Eigen (Eigen, Puhrsch, and Fergus 2014) split and the pre-processing method in (Zhou et al. 2017) to remove static frames, as in (Yin and Shi 2018; Zou, Luo, and Huang 2018; Ranjan et al. 2019). This provides 39,810 monocular triplets for training and 4,424 for the test. |
| Dataset Splits | Yes | This provides 39,810 monocular triplets for training and 4,424 for the test. |
| Hardware Specification | Yes | Our model is trained with a Titan XP GPU for 60 epochs using the Adam optimizer. |
| Software Dependencies | No | The total deep learning framework is implemented on Py Torch (Paszke et al. 2019). No specific version number for PyTorch or other key software components is provided. |
| Experiment Setup | Yes | We set w1 = 0.1 and w2 = 0.5 in Eq. (19). Our model is trained with a Titan XP GPU for 60 epochs using the Adam optimizer. We take a learning rate of 10 4 for the first 35 epochs and reduce it to 10 5 for the remainder. We set the batch size to 12 and the input/output resolution to 640 192 unless otherwise specified. |