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..
Unsupervised Learning of Shape Programs with Repeatable Implicit Parts
Authors: Boyang Deng, Sumith Kulal, Zhengyang Dong, Congyue Deng, Yonglong Tian, Jiajun Wu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical studies show that Pro GRIP outperforms existing structured representations in both shape reconstruction fidelity and segmentation accuracy of semantic parts. |
| Researcher Affiliation | Academia | Boyang Deng1, Sumith Kulal1, Zhengyang Dong1 Congyue Deng1 Yonglong Tian2 Jiajun Wu1 1Stanford University, 2MIT, equal contributions |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | We plan to release our source code upon publication. |
| Open Datasets | Yes | We conduct all our experiments using the Shape Net [6] dataset following Shape Net Terms of Use. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | In our experiments, we use λs = 1, λv = 0.2, and λe = 0.8 for all categories. |