用图像重建完整作物3D模型，解决遮挡难题
CropCraft: Complete Structural Characterization of Crop Plants From Images
- 通过逆向过程建模优化植物形态参数
- 重建的3D模型完整且符合生物真实结构
- 适用于农业监测与仿真，适合农科研究者
从图像自动构建植物的3D数字孪生在农业、环境科学和机器人等领域有广泛应用。然而，现有3D重建方法因严重遮挡和复杂几何结构，难以恢复植物完整形状。本文提出一种新方法，基于逆向过程建模，通过优化参数化植物形态模型实现3D建模。首先利用神经辐射场拟合深度图，再通过专用损失函数优化形态参数，使渲染深度与输入图像一致。最终生成的3D模型完整且具有生物学合理性。我们在真实农田图像数据集上验证了该方法，结果表明重建的冠层可用于多种监测与仿真任务。
原文摘要 · Abstract (English)
The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, current 3D reconstruction methods fail to recover complete shapes of plants due to heavy occlusion and complex geometries. In this work, we present a novel method for 3D modeling of agricultural crops based on optimizing a parametric model of plant morphology via inverse procedural modeling. Our method first estimates depth maps by fitting a neural radiance field and then optimizes a specialized loss to estimate morphological parameters that result in consistent depth renderings. The resulting 3D model is complete and biologically plausible. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructed canopies can be used for a variety of monitoring and simulation applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。