EPCPE: A Real-time End-to-End Pipeline for RGB-based Category-level 6D Pose Estimation

Abstract

RGB-based category-level 6D pose estimation methods have faced significant challenges in achieving real-time performance, primarily due to the design of two-stage pipeline. To address this issue, we propose a novel end-to-end pipeline named EPCPE. In detail, we first extract implicit rotation features via Large Visual Model (LVM), and then adaptively obtain pose-specific features with a fined-tuned Lite Feature Extractor. Finally, we introduce a novel Pose Decoder with two parallel branches, enabling simultaneous 6D pose estimation and 2D object detection. We also propose a novel rotation loss function to further enhance the performance. Extensive experiments on the CAMERA25 and REAL275 datasets demonstrate that our pipeline is concise, achieves state-of-the-art (SOTA) and real-time performance.