We present a generic algorithm for scoring pose estimation methods that rely
on single image semantic analysis. The algorithm employs a lightweight putative
shape representation using a combination of multiple Gaussian Processes. Each
Gaussian Process (GP) yields distance normal distributions from multiple
reference points in the object's coordinate system to its surface, thus
providing a geometric evaluation framework for scoring predicted poses. Our
confidence measure comprises the average mixture probability of pixel
back-projections onto the shape template. In the reported experiments, we
compare the accuracy of our GP based representation of objects versus the
actual geometric models and demonstrate the ability of our method to capture
the influence of outliers as opposed to the corresponding intrinsic measures
that ship with the segmentation and pose estimation methods.

通过使用多个高斯过程的组合，我们提出了一种用于评分依赖于单幅图像语义分析的姿态估计方法的通用算法。我们的算法利用了轻量级的推准形状表示，从物体的坐标系统中的多个参考点到其表面提供了距离正态分布，从而为评分预测姿态提供了几何评价框架。我们的置信度测量包括将像素反投影到形状模板上方的混合概率的平均值。在报告的实验证明中，我们将我们基于高斯过程的对象表示的准确性与实际几何模型进行了比较，并展示了我们的方法捕捉了与分割和姿态估计方法一起提供的相应内在度量的异常值的影响能力。