SO-PERM: Pose Estimation and Robust Measurement for Small Objects

One key challenge in object pose estimation is learn small object features under poor quality conditions while preventing false positives for object boundaries. The current study rarely focuses on small objects, and emphasizes specificity while ignores sensitivity. In this paper, we propose a multi-stage learning framework-SO-PERM to address these problems, which includes multiple modules like sub-pixel detection, 3D view reconstruction and multi-scale unsupervised network. Instead of using single object features, we choose to combine the noise distribution and boundary artifacts around the interference region, then refine the sub-pixels to clearly extract the target boundary. The 3D view reconstruction allows us to correlate the constructed 2D features with the real scene. The further obtained 3D heat map of simulated objects is not negligible for the pose estimation of tiny targets. In addition, considering the feature sensitivity of small targets, we propose a multi-scale unsupervised network and an iterative refinement scheme to improve performance, which combines global and local methods to quickly locate objects and extract specific features respectively. Extensive experiments on four benchmark sets not only demonstrate the robustness of SO-PERM, but also the feasibility of low-dimensional features to extend the idea of high-dimensional features.