自遮挡树冠骨架提取的遮挡推理方法
Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies
AI总结：
本文提出一种通过实例分割和三维似然图上的最小代价路径搜索来估计未观测结构，从而提取自遮挡树冠骨架的方法，实验证明其在高遮挡场景中优于基线方法。
摘要：
本文提出了一种通过估计树未被观测到的结构来提取自遮挡树冠骨架的方法。树骨架能够紧凑地描述树的拓扑结构，并包含诸如树枝几何形状、位置和层级等有用信息。这对于规划农业操作中的接触交互至关重要，但由于树叶、果实和其他树枝的遮挡，获取这些信息十分困难。我们的方法使用实例分割网络来检测可见的树干、树枝和细枝。然后，基于观测到的树结构，我们构建一个以占据栅格形式呈现的自定义三维似然图，通过一系列最小代价路径搜索来推测被遮挡骨架的存在。我们通过在一组合成树数据集上的实验表明，在高遮挡场景中，我们的方法优于基线方法。此外，我们还展示了在从田间采集的真实树数据集上的定性结果。
英文摘要：
In this work, we present a method to extract the skeleton of a self-occluded tree canopy by estimating the unobserved structures of the tree. A tree skeleton compactly describes the topological structure and contains useful information such as branch geometry, positions and hierarchy. This can be critical to planning contact interactions for agricultural manipulation, yet is difficult to gain due to occlusion by leaves, fruits and other branches. Our method uses an instance segmentation network to detect visible trunk, branches, and twigs. Then, based on the observed tree structures, we build a custom 3D likelihood map in the form of an occupancy grid to hypothesize on the presence of occluded skeletons through a series of minimum cost path searches. We show that our method outperforms baseline methods in highly occluded scenes, demonstrated through a set of experiments on a synthetic tree dataset. Qualitative results are also presented on a real tree dataset collected from the field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
