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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Authors: Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois Knoll
NeurIPS 2020 | Venue PDF | 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 EMAIL Guang Chen Tongji University EMAIL Yinlong Liu Technische Universität München EMAIL Zhongnan Qu ETH Zurich EMAIL Alois Knoll Technische Universität München EMAIL |
| 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. |