用3D高斯点渲染生成农机点云，仅靠合成数据就实现91.35%分割精度。
Point Cloud Segmentation of Agricultural Vehicles using 3D Gaussian Splatting
- 用3D高斯点渲染和透明度场生成真实感农机3D模型。
- 纯合成数据训练的Point Transformer V3模型达91.35% mIoU。
- 合成数据可跨类别泛化，且部分场景优于真实数据训练模型。
为解决3D点云语义分割需大量真实数据标注成本高的问题，本文提出一种基于3D高斯点渲染（3DGS）与高斯透明度场（GOF）的合成数据生成新方法，用于生成多种农业车辆的3D资产，并在模拟环境中通过仿真激光雷达生成点云。该方法灵活支持不同激光雷达参数配置，无需额外成本。我们仅使用合成数据训练并验证PointNet++、Point Transformer V3和OACNN等模型。结果显示，Point Transformer V3在未接触任何真实数据的情况下仍达到91.35%的mIoU，表现优异；进一步实验表明，在某些场景下纯合成数据训练的模型性能优于真实数据训练的模型。此外，模型具备跨语义类泛化能力，可在未训练过的网格模型上实现准确预测。
原文摘要 · Abstract (English)
Training neural networks for tasks such as 3D point cloud semantic segmentation demands extensive datasets, yet obtaining and annotating real-world point clouds is costly and labor-intensive. This work aims to introduce a novel pipeline for generating realistic synthetic data, by leveraging 3D Gaussian Splatting (3DGS) and Gaussian Opacity Fields (GOF) to generate 3D assets of multiple different agricultural vehicles instead of using generic models. These assets are placed in a simulated environment, where the point clouds are generated using a simulated LiDAR. This is a flexible approach that allows changing the LiDAR specifications without incurring additional costs. We evaluated the impact of synthetic data on segmentation models such as PointNet++, Point Transformer V3, and OACNN, by training and validating the models only on synthetic data. Remarkably, the PTv3 model had an mIoU of 91.35\%, a noteworthy result given that the model had neither been trained nor validated on any real data. Further studies even suggested that in certain scenarios the models trained only on synthetically generated data performed better than models trained on real-world data. Finally, experiments demonstrated that the models can generalize across semantic classes, enabling accurate predictions on mesh models they were never trained on.
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