PointGrade：用于MoonBoard问题难度评级的几何先验
PointGrade: Geometric Priors for Grading MoonBoard Problems
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中文总结 AI 辅助
PointGrade通过结合3D点云分类与序列方法，利用几何先验预测MoonBoard问题难度，优于忽略几何信息的方法。
中文摘要 AI 辅助
MoonBoard是一种标准化的抱石墙，在世界各地的健身房中使用。仅限使用部分支点的攀爬路线被称为问题。我们引入了PointGrade，一种新颖的机器学习方法，用于预测MoonBoard问题的难度。通过从每个支点的预扫描网格中采样点云，我们的模型将3D物体分类架构与现有的基于序列的难度等级预测方法相结合。我们的方法捕捉了攀爬路线中包含的潜在几何信息，在忽略这些数据的其他相关工作中表现更优。
英文摘要
A MoonBoard is a standardized bouldering wall used in gyms around the world. Climbs up the wall limited to only a subset of holds are known as problems. We introduce PointGrade, a novel machine learning approach to predicting the difficulty of a MoonBoard problem. By sampling a point cloud from pre-scanned meshes of every hold, our model combines 3D object classification architecture with existing sequence-based approaches to difficulty grade prediction. Our method captures latent geometric information contained the climb, outperforming other work on the problem that neglect this data.
发表机构
- Columbia University（哥伦比亚大学）
- Brown University（布朗大学）
机构由 AI 辅助整理，请以论文原文为准。
