用SfM技术将语义分割映射到森林点云，生成首个公开的标注数据集。
Mapping Semantic Segmentation to Point Clouds Using Structure from Motion for Forest Analysis
- 基于自建森林模拟器生成带语义标签的图像与分割图
- 改进开源SfM软件，实现3D重建中语义信息保留
- 为真实森林点云分割模型提供训练评估资源
尽管遥感技术在监测森林环境方面日益受到关注，但因采集成本高、传感器要求严及耗时长，公开可用的点云数据集仍然稀缺。据我们所知，尚无通过结构从运动（SfM）算法处理影像生成的公开标注数据集，这可能是因为缺乏能在复杂环境如森林中准确将语义分割信息映射到点云的SfM算法。本文提出一种新流程，用于生成森林环境的语义分割点云。利用自建森林模拟器，生成多样森林场景的真实RGB图像及其对应的语义分割掩码。这些带标签图像通过经修改的开源SfM软件处理，该软件可在三维重建过程中保留语义信息。最终生成的点云兼具几何与语义细节，为训练和评估旨在分割真实森林点云（通过SfM获取）的深度学习模型提供了宝贵资源。
原文摘要 · Abstract (English)
Although the use of remote sensing technologies for monitoring forested environments has gained increasing attention, publicly available point cloud datasets remain scarce due to the high costs, sensor requirements, and time-intensive nature of their acquisition. Moreover, as far as we are aware, there are no public annotated datasets generated through Structure From Motion (SfM) algorithms applied to imagery, which may be due to the lack of SfM algorithms that can map semantic segmentation information into an accurate point cloud, especially in a challenging environment like forests. In this work, we present a novel pipeline for generating semantically segmented point clouds of forest environments. Using a custom-built forest simulator, we generate realistic RGB images of diverse forest scenes along with their corresponding semantic segmentation masks. These labeled images are then processed using modified open-source SfM software capable of preserving semantic information during 3D reconstruction. The resulting point clouds provide both geometric and semantic detail, offering a valuable resource for training and evaluating deep learning models aimed at segmenting real forest point clouds obtained via SfM.
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