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..
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Authors: Minghua Liu, Ruoxi Shi, Kaiming Kuang, Yinhao Zhu, Xuanlin Li, Shizhong Han, Hong Cai, Fatih Porikli, Hao Su
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Open Shape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition. Specifically, Open Shape achieves a zero-shot accuracy of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than 10% for existing methods. Open Shape also achieves an accuracy of 85.3% on Model Net40, outperforming previous zero-shot baseline methods by 20% and performing on par with some fully-supervised methods. |
| Researcher Affiliation | Collaboration | Minghua Liu1 Ruoxi Shi2 Kaiming Kuang1 Yinhao Zhu3 Xuanlin Li1 Shizhong Han3 Hong Cai3 Fatih Porikli3 1 UC San Diego 2 Shanghai Jiao Tong University 3 Qualcomm AI Research |
| Pseudocode | No | The paper does not include any section or figure explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Project Website: https://colin97.github.io/Open Shape/ |
| Open Datasets | Yes | we ensemble four currently-largest public 3D datasets for training as shown in Figure 2 (a), resulting in 876k training shapes. Among these four datasets, Shape Net Core [8], 3D-FUTURE [16] and ABO [11] are three popular datasets used by prior works. They contain human-verified high-quality 3D shapes, but only cover a limited number of shapes and dozens of categories. The Objaverse [12] dataset is a more recent dataset |
| Dataset Splits | No | The paper describes the datasets used for training (ensembling Shape Net Core, 3D-FUTURE, ABO, and Objaverse) and the test splits of evaluation benchmarks (Model Net40 and Scan Object NN), but it does not specify explicit train/validation splits or cross-validation setup for its own model training and hyperparameter tuning. |
| Hardware Specification | Yes | We train the model on a single A100 GPU with a batch size of 200. |
| Software Dependencies | No | The paper mentions using 'Open CLIP Vi T-big G-14', 'BLIP', 'Azure cognition services', and 'GPT-4' but does not specify their version numbers or other software dependencies with version numbers. |
| Experiment Setup | Yes | We train the model on a single A100 GPU with a batch size of 200. ... For 32.3M version of Point BERT, we utilize a learning rate of 5e 4; for 72.1M version of Point BERT, we utilize a learning rate of 4e 4; and for other models, we utilize a learning rate of 1e 3. For hard-negative mining, the number of seed shapes s is set to 40, and the number of neighbors m is set to 5 per shape, and the threshold δ is set to 0.1. |