RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Authors: Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois Knoll
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two large scale outdoor Li DAR datasets show that the proposed RSKDD-Net achieves state-of-the-art performance with more than 15 times faster than existing methods. |
| Researcher Affiliation | Academia | Fan Lu Tongji University lufan@tongji.edu.cn Guang Chen Tongji University guangchen@tongji.edu.cn Yinlong Liu Technische Universität München Yinlong.Liu@tum.de Zhongnan Qu ETH Zurich quz@ethz.ch Alois Knoll Technische Universität München knoll@in.tum.de |
| Pseudocode | No | The paper describes the methods in text and uses diagrams (Fig. 1) to illustrate network architecture, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ispc-lab/RSKDD-Net. |
| Open Datasets | Yes | We evaluate our proposed RSKDD-Net on two large scale outdoor Li DAR datasets, namely KITTI Odometry Dataset [37] (KITTI dataset) and Ford Campus Vision and Lidar Dataset [38] (Ford dataset). |
| Dataset Splits | Yes | KITTI dataset provides 11 sequences (00-10) with ground truth vehicle poses and we use Sequence 00 to train, Sequence 01 for validation and the others for testing |
| Hardware Specification | Yes | The network is trained on NVIDIA GeForce 1080Ti and evaluated on a PC with Intel i7-9750H and NVIDIA GeForce RTX 2060. |
| Software Dependencies | No | The paper mentions "implemented using PyTorch [40]", but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The dilation ratio αd is set to 2 and the number of neighbor points is set to 128. The network is implemented using PyTorch [40]. We use SGD as the optimizer with learning rate of 0.001 and momentum of 0.9. Temperature t in matching loss is set to 0.1. |