Geometric-aware dense matching network for 6D pose estimation of objects from RGB-D images

6D pose estimation for certain targets from RGB-D images is a fundamental problem in computer vision. Current methods emphasize learning the overall expression of the targets, which leads to poor performance under occlusion and truncation conditions. In this paper, we propose using a geometric-aware dense matching network to obtain visible dense correspondences between a RGB-D image and 3D model to address difficult predictions from unseen keypoints. Two geometrical structures are considered for dense matching. (1) The neighbor area of the correspondences is treated as suboptimal matches in addition to the correspondence to reduce the influence of the error caused by ground truth calibration. (2) The distance consistency of the correspondences is leveraged to eliminate the ambiguity from the symmetrical objects. Experiments on LM-O dataset (77.1% ADD(S)-0.1d) and YCB-V dataset (97.6% ADD(S)) show the effectiveness and advantages of our proposed method.1