PointGrade: Geometric Priors for Grading MoonBoard Problems

2026-09-15

AI Summary

本文提出PointGrade方法，通过3D点云和序列模型预测MoonBoard攀岩难度，利用几何信息改进预测准确性。

Abstract

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.

Problem

• MoonBoard
• difficulty prediction
• geometric information

Innovation

• PointGrade
• geometric priors
• point cloud
• 3D object classification
• sequence-based approaches