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 [1].
MUCD: Unsupervised Point Cloud Change Detection via Masked Consistency
Authors: Yue Wu, Zhipeng Wang, Yongzhe Yuan, Maoguo Gong, Hao Li, Mingyang Zhang, Wenping Ma, Qiguang Miao
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on SLPCCD and Urb3DCD, two real-world datasets of streets and urban buildings, to verify that our proposed unsupervised method is highly competitive and even outperforms supervised methods in scenes where semantic information changes occur, exhibiting better performance in generalization ability and robustness. |
| Researcher Affiliation | Academia | 1Mo E Key Lab of Collaborative Intelligence Systems, Xidian University 2School of Computer Science and Technology, Xidian University 3Academy of Artificial Intelligence, College of Mathematics Science, Inner Mongolia Normal University 4School of Electronic Engineering, Xidian University 5School of Artificial Intelligence, Xidian University |
| Pseudocode | No | The paper describes the method using textual explanations and mathematical formulations, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any concrete access information for source code, such as a repository link or an explicit statement about code release in supplementary materials. |
| Open Datasets | Yes | Extensive experiments are conducted on SLPCCD and Urb3DCD, two real-world datasets of streets and urban buildings... we chose the street point cloud dataset SLPCCD (Wang et al. 2023), which is generated by a public dataset called Change3D... Another publicly available dataset is called Urb3DCD (de G elis, Lef evre, and Corpetti 2021). |
| Dataset Splits | No | In all experiments, we sample 8192 points for each point cloud for training and testing... We train the same segmentor using our method and labels on the SLPCCD training set, and test it on the Urb3DCD testing set. The paper mentions training and testing sets, but does not provide specific details on how these datasets are split (e.g., percentages, sample counts for each split). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory amounts, or detailed computer specifications used for running its experiments. |
| Software Dependencies | No | The paper does not specify any ancillary software or library names with their corresponding version numbers. |
| Experiment Setup | Yes | In all experiments, ... The k in all regional features related to feature metric learning in the main experiment is set to 8. When training the network, we use the Adam optimizer, set the batch size to 4 and set the initial learning rate to 0.001 reducing it exponentially (with a decay rate of 0.7). In the inference stage, we use a threshold of 0.5 credibility to segment the change points. In order to train the network normally, the epoch for feature initialization be set to 40, and the epoch for joint training should also is set to 40. |