{"ID":22921373,"CreatedAt":"2026-09-17T01:02:08.507062015Z","UpdatedAt":"2026-09-17T01:02:08.507062015Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.17770","arxiv_id":"2609.17770","title":"PointGrade: Geometric Priors for Grading MoonBoard Problems","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.","short_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 ev...","url_abs":"https://arxiv.org/abs/2609.17770","url_pdf":"https://arxiv.org/pdf/2609.17770v1","authors":"[\"Beatrice Stotz\",\"Ningna Wang\",\"Daria Nogina\",\"Caroline Zhang\",\"Jiyang Yin\",\"Amy Huang\",\"Ben Yang\",\"Jace Li\",\"Joel Salzman\",\"Steven Feiner\",\"Silvia Sellán\"]","published":"2026-09-15T19:21:44Z","proceeding":"cs.GR","tasks":"[\"cs.GR\"]","methods":"[]","has_code":false}