DFFNet: Dual-Channel Feature Fusion Network for End-to-End 6D Pose Estimation

Recently, the popular RGB-based 6D pose estimation methods follow the two-stage paradigm, in which the sparse or dense 2D-3D correspondences between the RGB image and the CAD model are first established, and then the 6D pose is estimated based on the PnP/RANSAC algorithm or its variants. Although such methods are considered effective and achieve excellent performance, this two-stage pipeline cannot be end-to-end trainable and is time-consuming. In this work, we propose a one-stage Dual-Channel Feature Fusion Network for End-to-End 6D Pose Estimation (DFFNet), in which an Implicit Pose Feature Extraction Channel is designed to extract the implicit pose features inside each 2D-3D correspondence and an Implicit Topological Feature Extraction Channel is designed to extract the geometric topological features implicitly expressed by each group of 2D-3D correspondences. The different features extracted by the two channels are fused to regress the 6D pose of the object. DFFNet is generic and can be combined with the existing sparse 2D-3D correspondence-extraction network to eliminate the cumbersome RANSAC process and form an overall end-to-end trainable framework. Experiments show that our proposed method outperforms the one-stage and some other two-stage 6D pose estimation methods.