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..
CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame
Authors: Yujing Lou, Zelin Ye, Yang You, Nianjuan Jiang, Jiangbo Lu, Weiming Wang, Lizhuang Ma, Cewu Lu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method achieves rotation invariance, accurately estimates the object rotation, and obtains state-of-the-art results on rotation-augmented classification and part segmentation. ... In this section, we evaluate CRIN on several 3D object datasets and conduct the ablation study. |
| Researcher Affiliation | Collaboration | Yujing Lou1, Zelin Ye1, Yang You1, Nianjuan Jiang2, Jiangbo Lu2, Weiming Wang1, Lizhuang Ma1*, Cewu Lu1* 1 Shanghai Jiao Tong University 2 Smart More |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks with structured steps formatted like code. |
| Open Source Code | No | The paper does not provide any concrete access information, such as a repository link or an explicit statement about the release of source code for the methodology described. |
| Open Datasets | Yes | We evaluate CRIN on Model Net40 dataset (Wu et al. 2015) for object classification. ... We use the Shape Net part dataset (Yi et al. 2016) for 3D part segmentation. |
| Dataset Splits | Yes | We follow (Qi et al. 2017a) to split the dataset into 9843 and 2468 point clouds for training and testing, respectively. ... The train/test splitting is according to (Qi et al. 2017a). |
| Hardware Specification | Yes | The experiments are conducted on a single Ge Force RTX 2080Ti GPU and an Intel(R) Core(TM) i9-7900X @ 3.30GHz CPU. |
| Software Dependencies | No | The paper mentions 'We use Adam (Kingma and Ba 2014) optimizer', but it does not specify any software components with their version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | Yes | We use Adam (Kingma and Ba 2014) optimizer during training and set the initial learning rate as 0.001. The batch size is 32, with about 2 minutes per training epoch on one GPU. |