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In the current state of 6D pose estimation, top-performing techniques dependon complex intermediate correspondences, specialized architectures, andnon-end-to-end algorithms. In contrast, our research reframes the problem as astraightforward regression task by exploring the capabilities of VisionTransformers for direct 6D pose estimation through a tailored use ofclassification tokens. We also introduce a simple method for determining poseconfidence, which can be readily integrated into most 6D pose estimationframeworks. This involves modifying the transformer architecture by decreasingthe number of query elements based on the network's assessment of the scenecomplexity. Our method that we call Pose Vision Transformer or PViT-6D providesthe benefits of simple implementation and being end-to-end learnable whileoutperforming current state-of-the-art methods by +0.3% ADD(-S) onLinemod-Occlusion and +2.7% ADD(-S) on the YCB-V dataset. Moreover, our methodenhances both the model's interpretability and the reliability of itsperformance during inference.

PViT-6D: Overclocking Vision Transformers for 6D Pose Estimation with Confidence-Level Prediction and Pose Tokens