Multiscale-Hashing Network for 6-D Pose Estimation of Unseen Objects in the Wild

Abstract

Estimating the pose of unseen objects is a fundamental task in robotics and industrial automation. Some methods rely on prior knowledge of individual objects for this task and require the model to be trained on specific object instances or categories. Other methods can estimate the pose of unseen objects but are often limited in handling occlusions. To address these challenges, we propose an unseen object pose estimation method for objects not encountered during training. We propose a Hashing Attention Network that integrates features from multiple scales to effectively capture global information while maintaining high sensitivity to local positional details, thereby significantly enhancing the model’s predictive accuracy for occluded objects. We incorporate Locality Sensitive Hashing into the self-attention mechanism of the vision transformer. This approach significantly reduces computational complexity by computing attention for tokens at each scale based on hash similarity, both within the same scale and across multiple scales. The network was trained on selected BOP datasets, and the Linemod, T-LESS, and Wild6D datasets were used to evaluate pose estimation on unseen objects. Our results show that the proposed model outperforms existing methods across various quantitative metrics, making it well suited for industrial applications.