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..
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
Authors: Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, Ling Shao
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that Polar Mix achieves superior performance consistently across different perception tasks and scenarios. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Nanyang Technological University 2Mohamed bin Zayed University of Artificial Intelligence 3Terminus Group, China |
| Pseudocode | Yes | Algorithm 1 Polar Mix. |
| Open Source Code | Yes | Code is available at https://github.com/xiaoaoran/polarmix |
| Open Datasets | Yes | The first is Semantic KITTI [1]... The second is nu Scenes-lidarseg [12] dataset... The third is Semantic POSS [30]... Syn Li DAR [46] is a synthetic Li DAR point cloud dataset... |
| Dataset Splits | Yes | Semantic KITTI: We follow the widely-adopted split and use sequences 00-07, 09-10 as the training set and sequence 08 for validation. ... nu Scenes-lidarseg: We follow the officially split of training data and validation data. ... Semantic POSS: We follow the official benchmark setting, i.e. sequence 03 for validation and the rest for training. |
| Hardware Specification | Yes | We conducted experiments with a single Tesla 2080Ti GPU for Mink Net and SPVCNN and a Tesla V100 GPU for Rand LA-Net and Cylinder3D. |
| Software Dependencies | No | The paper mentions using open-source repositories for various networks (e.g., Mink Net, SPVCNN, Rand LA-Net, Cylinder3D, Open PCDet) but does not provide specific version numbers for any software dependencies like libraries or frameworks. |
| Experiment Setup | Yes | We adopt the default training hyper-parameters in the open-source repositories for all four networks, and the only modification is the batch size for SPVCNN and Mink Net (we change it to 8). ... For augmentation with scene-level swapping, we randomly crop 180 sectors from 360 for [α, β] for point swapping. ... We set δ1, δ2 as 0.5, 1, respectively. |