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..
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
Authors: Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, Bernard Ghanem
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through a comprehensive empirical study on various benchmarks, e.g., Scan Objec NN [42] for object classification and S3DIS [1] for semantic segmentation, we discover that training strategies, i.e., data augmentation and optimization techniques, play an important role in the network s performance. |
| Researcher Affiliation | Collaboration | 1King Abdullah University of Science and Technology (KAUST), 2Microsoft Research |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures), only architectural diagrams. |
| Open Source Code | Yes | The code and models are available at https://github.com/guochengqian/pointnext. |
| Open Datasets | Yes | We evaluate Point Ne Xt on five standard benchmarks: S3DIS [1] and Scan Net [5] for semantic segmentation, Scan Object NN [42] and Model Net40 [47] for object classification, and Shape Net Part [3] for object part segmentation. |
| Dataset Splits | Yes | Scan Net [5]... We follow the public training, validation, and test splits, with 1201, 312 and 100 scans, respectively. |
| Hardware Specification | Yes | We train Point Ne Xt using... a 32G V100 GPU, for all tasks, unless otherwise specified. |
| Software Dependencies | No | The paper mentions using 'Cross Entropy loss with label smoothing [37]', 'Adam W optimizer [25]', and 'Poly Focal Loss [17]' and refers to a PyTorch reimplementation [50] for baseline, but it does not specify exact version numbers for the software libraries or frameworks used (e.g., PyTorch version, CUDA version). |
| Experiment Setup | Yes | We train Point Ne Xt using Cross Entropy loss with label smoothing [37], Adam W optimizer [25], an initial learning rate lr = 0.001, weight decay 10 4, with Cosine Decay, and a batch size of 32, with a 32G V100 GPU, for all tasks, unless otherwise specified. ... for 100 epochs (training set is repeated by 30 times), using a fixed number of points (24, 000) per batch with a batch size of 8 as input. |