We present a new approach for a single view, image-based object pose estimation. Specifically, the problem of culling false positives among several pose proposal estimates is addressed in this paper. Our proposed approach targets the problem of inaccurate confidence values predicted by CNNs which is used by many current methods to choose a final object pose prediction. We present a network called CullNet, solving this task. CullNet takes pairs of pose masks rendered from a 3D model and cropped regions in the original image as input. This is then used to calibrate the confidence scores of the pose proposals. This new set of confidence scores is found to be significantly more reliable for accurate object pose estimation as shown by our results. Our experimental results on multiple challenging datasets (LINEMOD and Occlusion LINEMOD) reflects the utility of our proposed method. Our overall pose estimation pipeline outperforms state-of-the-art object pose estimation methods on these standard object pose estimation datasets. Our code is publicly available on https://github.com/kartikgupta-at-anu/CullNet.

本研究提出了一种用于基于图像的单视角物体姿态估计的新方法，称为CullNet，针对当前许多方法中使用的不准确的CNN预测的置信度值，提出了一个解决姿态提议估计问题的方法，该方法使用来自3D模型和原始图像中裁剪区域的姿态掩码对进行调整，实现了更加可靠的精确物体姿态估计，和现有姿态估计方法相比，我们的方法表现出更好的性能。

CullNet: 物体位姿估计的校准和姿态感知置信度分数