A Geometry-Enhanced 6D Pose Estimation Network With Incomplete Shape Recovery for Industrial Parts

Accurate and robust 6-DOF (6D) pose estimation from a single RGB image and depth map (RGB-D) image is an essential task of intelligent manufacturing, such as robot assembly and digital twin. However, incomplete and noisy 3-D data acquired from depth sensors make the task challenging, especially for various industrial parts without sufficient textures, where the occlusion further exacerbates the problem. To tackle this issue, this article proposes a geometry-enhanced network with incomplete shape recovery (GER-Net) to estimate the 6D pose of industrial parts. First, an incomplete 3-D shape recovery (ISR) module with a learnable shape protection (SP) layer is introduced to recover the complete 3-D geometry shapes of raw point clouds obtained from depth measurements. Subsequently, the multimodal features extracted from raw RGB-D data are enhanced with the geometry information from the recovered point cloud via multiscale concatenation and recurrent forward fusion in the point cloud space. In this way, the enhanced RGB-D representations contribute to the regression of accurate 6D pose. Experiments on two popular benchmark datasets (LineMOD and Occlusion-LineMOD) show that the proposed approach achieves state-of-the-art performance. Furthermore, a real-world low-texture industrial part dataset industrial texture-less machined and 3-D-printed parts (ITM3D) is presented to fully validate the effectiveness of our method, where it also achieves the best performance with remarkable accuracy and robustness.

Automated summary

This article proposes a geometry-enhanced network with incomplete shape recovery (GER-Net) to estimate the 6D pose of industrial parts. Incomplete and noisy 3D data are recovered using a learnable shape protection layer, and the enhanced RGB-D representations contribute to the regression of accurate 6D pose. Experimental results show that the proposed approach achieves state-of-the-art performance on benchmark datasets and exhibits remarkable accuracy and robustness on a real-world low-texture industrial part dataset.