用高斯斑点构建植物生长的动态数字孪生模型。
GrowSplat: Constructing Temporal Digital Twins of Plants with Gaussian Splats
- 结合多视角图像与高斯斑点重建，分两阶段对齐实现4D动态建模。
- 在荷兰植物表型中心数据上成功还原巨杉与藜麦的逐时生长过程。
- 适合植物表型分析、育种研究及动态生物结构建模的科研人员。
精准的植物生长时序重建对植物表型分析与育种至关重要，但受限于复杂的几何形态、遮挡和非刚性形变而极具挑战。本文提出一种新框架，通过将3D高斯斑点（Gaussian Splatting）与鲁棒样本对齐流程结合，构建植物的时序数字孪生。方法从多视角相机数据中重建高斯斑点，采用两阶段配准：先基于特征匹配与快速全局配准进行粗对齐，再通过迭代最近点（ICP）实现精对齐，从而生成离散时间步下的统一4D模型。我们在荷兰植物生态表型中心的数据上评估该方法，成功实现了巨杉（Sequoia）与藜麦（Quinoa）物种的详细时序重建。相关视频与图像可访问 https://berkeleyautomation.github.io/GrowSplat/。
原文摘要 · Abstract (English)
Accurate temporal reconstructions of plant growth are essential for plant phenotyping and breeding, yet remain challenging due to complex geometries, occlusions, and non-rigid deformations of plants. We present a novel framework for building temporal digital twins of plants by combining 3D Gaussian Splatting with a robust sample alignment pipeline. Our method begins by reconstructing Gaussian Splats from multi-view camera data, then leverages a two-stage registration approach: coarse alignment through feature-based matching and Fast Global Registration, followed by fine alignment with Iterative Closest Point. This pipeline yields a consistent 4D model of plant development in discrete time steps. We evaluate the approach on data from the Netherlands Plant Eco-phenotyping Center, demonstrating detailed temporal reconstructions of Sequoia and Quinoa species. Videos and Images can be seen at https://berkeleyautomation.github.io/GrowSplat/
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