Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration
Authors: KeZheng Xiong, Haoen Xiang, Qingshan Xu, Chenglu Wen, Siqi Shen, Jonathan Jun LI, Cheng Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on KITTI and nu Scenes datasets demonstrate that our INTEGER achieves competitive performance in terms of accuracy and generalizability. |
| Researcher Affiliation | Academia | a Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China. b Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China. c Nanyang Technological University, Singapore. d University of Waterloo, Waterloo, Canada |
| Pseudocode | Yes | Algorithm 1: Feature-Geometry Clustering |
| Open Source Code | Yes | [Code Release] |
| Open Datasets | Yes | We mainly evaluate INTEGER on two challenging public datasets: KITTI[6] and nu Scenes[7]. |
| Dataset Splits | Yes | Both datasets adhere to official splits. |
| Hardware Specification | Yes | The training process takes approximately 6 days on a single NVIDIA RTX 3090 GPU running at 1.70 GHz with 24 Gi B of GPU memory. |
| Software Dependencies | No | The paper mentions using FCGF, SC2-PCR, and the sklearn library for t-SNE and KDE, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | To train INTEGER, we use the SGD optimizer with an initial learning rate of 0.3 and a weight decay of 1e 4. We train INTEGER for 400 epochs with a batch size of 8. |