Self-Supervised Few-Shot Learning on Point Clouds
Authors: Charu Sharma, Manohar Kaul
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a comprehensive empirical evaluation of our method on both downstream classification and segmentation tasks and show that supervised methods pre-trained with our self-supervised learning method significantly improve the accuracy of state-of-the-art methods. |
| Researcher Affiliation | Academia | Charu Sharma and Manohar Kaul Department of Computer Science & Engineering Indian Institute of Technology Hyderabad, India charusharma1991@gmail.com, mkaul@iith.ac.in |
| Pseudocode | No | The paper describes the network architecture and methods but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'Our code' in a footnote (footnote 3) but does not provide a specific repository link or an explicit statement of public availability for the source code. |
| Open Datasets | Yes | For self-supervised and FSL experiments, we pick two real-world datasets (Model Net40 [15] and Sydney4) for 3D shape classification and for our segmentation related experiments, we conduct part segmentation on Shape Net [24] and semantic segmentation on Stanford Large-Scale 3D Indoor Spaces (S3DIS) [25]. |
| Dataset Splits | No | The paper describes training with a 'support set S' and testing with a 'query set Q', but does not explicitly mention a 'validation' set or its specific split for reproduction purposes. |
| 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 mentions software like Point Net, DGCNN, and linear SVM, but does not provide specific version numbers for any software dependencies or frameworks. |
| Experiment Setup | No | The paper describes network architecture layer sizes (e.g., MLP layers with 32, 64, 128 dimensions) and discusses the choice of the expansion constant ϵ (e.g., ϵ = 2.2). However, it lacks specific training hyperparameters such as learning rate, batch size, optimizer details, or number of epochs, which are crucial for complete reproduction. |