EfficientPose 6D: Scalable and Efficient 6D Object Pose Estimation
This work addresses the need for scalable and efficient pose estimation in industrial settings like quality control and robotic manipulation, though it appears incremental as it builds on existing GDRNPP methods.
The study tackled the problem of balancing computational efficiency and accuracy in 6D object pose estimation for real-time industrial applications, proposing the AMIS algorithm to tailor models for specific trade-offs and demonstrating effectiveness on four benchmark datasets.
In 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 (SOTA) 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).