Abstract: To solve the problems of insufficient feature and pose ambiguity caused by texture-less and symmetric objects in complex scenes during 6D pose estimation,a pose estimation network named SGF-Pose integrating frequency-domain feature enhancement and geometric enhancement is proposed.Firstly,a spectral feature enhancement module(SFEM) is introduced in the feature extraction stage.By learning a spectral mask,background high-frequency noise is adaptively suppressed to improve the robustness of the predictions.Furthermore,a geometry enhancement module(GEM) is designed to explicitly measure pixel-level geometric curvature by calculating the spatial gradients of coordinate mappings.Finally,the 6D pose is predicted through a differentiable PnP solver module by combining dense prediction maps and geometric confidence.Experimental results show that,using the ADD-(S) metric,SGF-Pose achieves an accuracy of 96.9% on the LineMOD dataset,which is 1.7 percentage points higher than that of DPOD.On the Occlusion LineMOD dataset,an accuracy of 59.5% is achieved,representing an improvement of 3.4 percentage points over GDR-Net.Using the AR metric,an accuracy of 66.4% is achieved by SGF-Pose on the T-LESS dataset,which is 2.4 percentage points higher than that of CosyPose.These results indicate that SGF-Pose performs well in pose estimation for weak-texture and symmetric scenes.

Key words: Pose estimation, Frequency domain analysis, Feature fusion, Texture-less object, Deep learning