改进3D点云分类数据集与模型，提升精度与效率
Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet
- 重构ModelNet40为更精准的ModelNet-R数据集
- 提出轻量图神经网络Point-SkipNet，参数少且准确率高
- 适合关注3D点云模型优化的研究者与工程师
3D点云分类在自动驾驶、机器人和增强现实等应用中至关重要。然而，常用的ModelNet40数据集存在标注不一致、包含2D数据、尺寸不匹配及类别区分度不足等问题，影响模型性能。本文提出ModelNet-R，对ModelNet40进行精细化修复，作为更可靠的基准。同时提出Point-SkipNet，一种基于图结构的轻量级神经网络，通过高效采样、邻域分组和跳跃连接，在降低计算开销的同时实现高分类精度。大量实验表明，基于ModelNet-R训练的模型性能显著提升。特别地，Point-SkipNet在ModelNet-R上达到当前最优准确率，参数量远低于现有模型。研究强调了数据质量对3D点云分类模型效率优化的关键作用。
原文摘要 · Abstract (English)
The classification of 3D point clouds is crucial for applications such as autonomous driving, robotics, and augmented reality. However, the commonly used ModelNet40 dataset suffers from limitations such as inconsistent labeling, 2D data, size mismatches, and inadequate class differentiation, which hinder model performance. This paper introduces ModelNet-R, a meticulously refined version of ModelNet40 designed to address these issues and serve as a more reliable benchmark. Additionally, this paper proposes Point-SkipNet, a lightweight graph-based neural network that leverages efficient sampling, neighborhood grouping, and skip connections to achieve high classification accuracy with reduced computational overhead. Extensive experiments demonstrate that models trained in ModelNet-R exhibit significant performance improvements. Notably, Point-SkipNet achieves state-of-the-art accuracy on ModelNet-R with a substantially lower parameter count compared to contemporary models. This research highlights the crucial role of dataset quality in optimizing model efficiency for 3D point cloud classification. For more details, see the code at: https://github.com/m-saeid/ModeNetR_PointSkipNet.
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