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.

翻译：摘要：本文提出一种通用算法，用于评估依赖单张图像语义分析的位姿估计方法。该算法采用轻量级假定形状表示，结合了多个高斯过程。每个高斯过程从目标坐标系中的多个参考点出发，生成到目标表面的距离正态分布，从而为预测位姿的评分提供几何评估框架。我们的置信度度量包括像素反投影到形状模板上的平均混合概率。在实验报告中，我们比较了基于高斯过程的目标表示与实际几何模型的准确性，并展示了我们的方法捕捉异常值影响的能力，这与分割和位姿估计方法所附带的内在度量形成对比。