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..
Explore In-Context Learning for 3D Point Cloud Understanding
Authors: Zhongbin Fang, Xiangtai Li, Xia Li, Joachim M Buhmann, Chen Change Loy, Mengyuan Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to validate the versatility and adaptability of our proposed methods in handling a wide range of tasks. |
| Researcher Affiliation | Academia | 1Sun Yat-sen University 2S-Lab, Nanyang Technological University 3Department of Computer Science, ETH Zurich 4Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University |
| Pseudocode | No | The paper describes its methods through text and diagrams, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://github.com/fanglaosi/Point-In-Context |
| Open Datasets | Yes | Firstly, we obtain samples from publicly available datasets, such as Shape Net [7], Shape Net Part [42] |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about a distinct validation set split or its size/percentage for reproduction. |
| Hardware Specification | Yes | Test speed ... tested on one NVIDIA RTX 3080 Ti GPU. |
| Software Dependencies | No | The paper mentions the use of 'Adam W optimizer' and 'standard transformer', but does not specify versions of programming languages, libraries, or frameworks used for implementation, such as PyTorch or TensorFlow versions. |
| Experiment Setup | Yes | We sample 1024 points of each point cloud and divide it into N = 64 point patches, each with M = 32 neighborhood points. We set the mask ratio as 0.7. For PIC-Sep, we merge the feature of input and target at the third block. We randomly select a prompt pair that performs the same task with the query point cloud from the training set. We use an Adam W optimizer [23] and cosine learning rate decay, with the initial learning rate as 0.001 and a weight decay of 0.05. All models are trained for 300 epochs. |