PAPER / ARXIV:2609.17770
Beatrice Stotz , Ningna Wang , Daria Nogina , Caroline Zhang , Jiyang Yin , Amy Huang , Ben Yang , Jace Li , Joel Salzman , Steven Feiner , Silvia Sellán
RESUMO
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.
NO MESMO MAPA