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..
Learning Part Generation and Assembly for Structure-Aware Shape Synthesis
Authors: Jun Li, Chengjie Niu, Kai Xu11362-11369
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate through both qualitative and quantitative evaluations that PAGENet generates 3D shapes with plausible, diverse and detailed structure |
| Researcher Affiliation | Academia | Jun Li, Chengjie Niu, Kai Xu* National University of Defense Technology |
| Pseudocode | No | The paper describes the network architecture and training details in text and figures, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is available or provide a link to a code repository. |
| Open Datasets | Yes | We train and test our model on the Shape Net part dataset (Yi et al. 2016), which is a subset of the Shape Net dataset (Chang et al. 2015) and provides consistent alignment and semantic labeling for all shapes. |
| Dataset Splits | Yes | The dataset is divided into two parts, according to the official training/test split. |
| Hardware Specification | No | The paper mentions training times ('average training time is 12 hours for each part generator and 7 hours for part assembler') but does not specify any hardware details like GPU or CPU models. |
| Software Dependencies | No | The paper mentions using ADAM for optimization and WGAN-GP for adversarial training, but does not provide specific version numbers for any software, libraries, or frameworks used. |
| Experiment Setup | Yes | For all modules, we use ADAM (β = 0.5) for network optimization with an initial learning rate of 0.001. Batch size is set to 32. The parameters in the loss in Equation (1) are set as α1 = 2 and α2 = 1 10 3 for all experiments. The λ in Equation (2) is set to 10 as in (Gulrajani et al. 2017). |