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..
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 | Venue PDF | 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. |