Fang, ZixuanZixuanFangPöllabauer, ThomasThomasPöllabauerWirth, TristanTristanWirthBerkei, SarahSarahBerkeiKnauthe, VolkerVolkerKnautheKuijper, ArjanArjanKuijper2025-07-242025-07-242025https://publica.fraunhofer.de/handle/publica/48994310.1007/978-3-031-95918-9_14In industrial applications requiring real-time feedback, such as quality control and robotic manipulation, the demand for high-speed and accurate pose estimation remains critical. Despite advances improving speed and accuracy in pose estimation, finding a balance between computational efficiency and accuracy poses significant challenges in dynamic environments. Most current algorithms lack scalability in estimation time, especially for diverse datasets, and the state-of-the-art methods are often too slow. This study focuses on developing a fast and scalable set of pose estimators based on GDRNPP to meet or exceed current benchmarks in accuracy and robustness, particularly addressing the efficiency-accuracy trade-off essential in real-time scenarios. We propose the AMIS algorithm to tailor the utilized model according to an application-specific trade-off between inference time and accuracy. We further show the effectiveness of the AMIS-based model choice on four prominent benchmark datasets (LM-O, YCB-V, T-LESS, and ITODD).enBranche: Information TechnologyResearch Line: Computer vision (CV)Research Line: Modeling (MOD)LTA: Machine intelligence, algorithms, and data structures (incl. semantics)Object pose estimationMachine learningEfficiencyconference paper