{"ID":2867136,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18979","arxiv_id":"2509.18979","title":"Category-Level Object Shape and Pose Estimation in Less Than a Millisecond","abstract":"Object shape and pose estimation is a foundational robotics problem, supporting tasks from manipulation to scene understanding and navigation. We present a fast local solver for shape and pose estimation which requires only category-level object priors and admits an efficient certificate of global optimality. Given an RGB-D image of an object, we use a learned front-end to detect sparse, category-level semantic keypoints on the target object. We represent the target object's unknown shape using a linear active shape model and pose a maximum a posteriori optimization problem to solve for position, orientation, and shape simultaneously. Expressed in unit quaternions, this problem admits first-order optimality conditions in the form of an eigenvalue problem with eigenvector nonlinearities. Our primary contribution is to solve this problem efficiently with self-consistent field iteration, which only requires computing a 4-by-4 matrix and finding its minimum eigenvalue-vector pair at each iterate. Solving a linear system for the corresponding Lagrange multipliers gives a simple global optimality certificate. One iteration of our solver runs in about 100 microseconds, enabling fast outlier rejection. We test our method on synthetic data and a variety of real-world settings, including two public datasets and a drone tracking scenario. Code is released at https://github.com/MIT-SPARK/Fast-ShapeAndPose.","short_abstract":"Object shape and pose estimation is a foundational robotics problem, supporting tasks from manipulation to scene understanding and navigation. We present a fast local solver for shape and pose estimation which requires only category-level object priors and admits an efficient certificate of global optimality. Given an...","url_abs":"https://arxiv.org/abs/2509.18979","url_pdf":"https://arxiv.org/pdf/2509.18979v2","authors":"[\"Lorenzo Shaikewitz\",\"Tim Nguyen\",\"Luca Carlone\"]","published":"2025-09-23T13:29:32Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":609442,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2867136,"paper_url":"https://arxiv.org/abs/2509.18979","paper_title":"Category-Level Object Shape and Pose Estimation in Less Than a Millisecond","repo_url":"https://github.com/MIT-SPARK/Fast-ShapeAndPose","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}