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..
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery
Authors: Weibing Zhao, Xu Yan, Jiantao Gao, Ruimao Zhang, Jiayan Zhang, Zhen Li, Song Wu, Shuguang Cui
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Point LIE outperforms the state-of-the-art point cloud sampling and upsampling methods both quantitatively and qualitatively. To quantitatively evaluate the performance of different methods, three commonly-used metrics are adopted, i.e., Chamfer distance (CD), Hausdorff distance (HD) and point-to-surface distance (P2F). |
| Researcher Affiliation | Academia | Weibing Zhao1,2 , Xu Yan1,2 , Jiantao Gao2,3, Ruimao Zhang1,2, Jiayan Zhang1, Zhen Li1,2 , Song Wu4, Shuguang Cui1,2 1 The Chinese University of Hong Kong, Shenzhen 2 Shenzhen Research Institute of Big Data 3 Shanghai University 4 Shenzhen Luohu Hospital {weibingzhao@link., xuyan1@link., lizhen@}cuhk.edu.cn |
| Pseudocode | No | The paper describes the model architecture and processes using figures and mathematical equations (e.g., Eq. 3, 4, 5, 6) but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | No explicit statement about open-sourcing code or a link to a code repository is provided. |
| Open Datasets | Yes | To fully evaluate the proposed Point LIE, we compared our method with the state-of-the-art methods on PU-147 [Li et al., 2019] dataset, which follows the official split of 120/27 for our training and testing sets. |
| Dataset Splits | No | The paper mentions 'PU-147 [Li et al., 2019] dataset, which follows the official split of 120/27 for our training and testing sets.' but does not explicitly detail the exact percentages or counts for training, validation, and test splits, nor does it explicitly mention a validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA, or library versions) are mentioned in the paper. |
| Experiment Setup | Yes | Under the premise of balancing efficiency and effectiveness, we set PI block number M as 8 in the 4 scale task, and M as 4 in the rest 8 and 16 tasks. Furthermore, we set k as 3 to ensure that the information in the discarded points can be sufficiently preserved. |