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..
Attention-Based Transformation from Latent Features to Point Clouds
Authors: Kaiyi Zhang, Ximing Yang, Yuan Wu, Cheng Jin3291-3299
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Considerable experiments on different datasets show that our methods achieve state-of-the-art results. and sections like Experiments, Datasets and Implementation Details, Reconstruction and Generation. |
| Researcher Affiliation | Academia | 1School of Computer Science, Fudan University, Shanghai, China 2Peng Cheng Laboratory, Shenzhen, China EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | We evaluate AXform on three representative categories in the Shape Net (Chang et al. 2015) dataset: airplane, car, and chair. and We also evaluate AXform Net on the PCN (Yuan et al. 2018) dataset for point cloud completion. |
| Dataset Splits | Yes | We follow the train/val/test split in Shape Net official documents and use 2048 points for each shape during both training and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'Adam is used as the optimizer' but does not specify any software names with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | All the experiments are performed for 200 epochs with a batch size of 32. Adam is used as the optimizer and the initial learning rate is 1e-4. and We set branch number K = 16 and train our method for 100 epochs with a batch size of 128. α increases from 0.01 to 1 within the first 25 epochs. Adam is used as the optimizer and the initial learning rate is 1e-3. |