{"ID":2846071,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02277","arxiv_id":"2511.02277","title":"Are Euler angles a useful rotation parameterisation for pose estimation with Normalizing Flows?","abstract":"Object pose estimation is a task that is of central importance in 3D Computer Vision. Given a target image and a canonical pose, a single point estimate may very often be sufficient; however, a probabilistic pose output is related to a number of benefits when pose is not unambiguous due to sensor and projection constraints or inherent object symmetries. With this paper, we explore the usefulness of using the well-known Euler angles parameterisation as a basis for a Normalizing Flows model for pose estimation. Isomorphic to spatial rotation, 3D pose has been parameterized in a number of ways, either in or out of the context of parameter estimation. We explore the idea that Euler angles, despite their shortcomings, may lead to useful models in a number of aspects, compared to a model built on a more complex parameterisation.","short_abstract":"Object pose estimation is a task that is of central importance in 3D Computer Vision. Given a target image and a canonical pose, a single point estimate may very often be sufficient; however, a probabilistic pose output is related to a number of benefits when pose is not unambiguous due to sensor and projection constra...","url_abs":"https://arxiv.org/abs/2511.02277","url_pdf":"https://arxiv.org/pdf/2511.02277v1","authors":"[\"Giorgos Sfikas\",\"Konstantina Nikolaidou\",\"Foteini Papadopoulou\",\"George Retsinas\",\"Anastasios L. Kesidis\"]","published":"2025-11-04T05:28:02Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}