Reliable Inlier Evaluation for Unsupervised Point Cloud Registration
Authors: Yaqi Shen, Le Hui, Haobo Jiang, Jin Xie, Jian Yang2198-2206
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance. We evaluate our method on extensive benchmark datasets, including Model Net40 (Wu et al. 2015), 7Scenes (Shotton et al. 2013), ICL-NUIM (Choi, Zhou, and Koltun 2015), and KITTI (Geiger, Lenz, and Urtasun 2012) and the experimental results verify the effectiveness of our method. We conduct ablation study on two key components of our model: Matching Map Reļ¬nement (MMR) and Inlier Evaluation (IE) modules. |
| Researcher Affiliation | Academia | PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China {syq, le.hui, jiang.hao.bo, csjxie, csjyang}@njust.edu.cn |
| Pseudocode | No | No pseudocode or algorithm blocks found. |
| Open Source Code | No | No explicit statement about providing open-source code or a link to a repository for the described methodology. |
| Open Datasets | Yes | We evaluate our method on Model Net40 (Wu et al. 2015), 7Scenes (Shotton et al. 2013), ICL-NUIM (Choi, Zhou, and Koltun 2015) and KITTI odometry datasets (Geiger, Lenz, and Urtasun 2012). |
| Dataset Splits | Yes | And the KITTI odometry dataset consists of 11 sequences with ground truth pose, we use Sequence 00-05 for training, 06-07 for validation, and 08-10 for testing. |
| Hardware Specification | Yes | We calculate the inference time with an Intel I5-8400 CPU and Geforce RTX 2080Ti GPU. |
| Software Dependencies | No | Our model is implemented in Pytorch. No specific version numbers for PyTorch or other software dependencies are provided. |
| Experiment Setup | Yes | We optimize the parameters with the ADAM optimizer. The initial learning rate is 0.001. For Model Net40 and KITTI, we train the network for 50 epochs and multiply the learning rate by 0.7 at epoch 25. For indoor scenes, we multiply the learning rate by 0.7 at epochs 25, 50, 75 and train the network for 100 epochs. |