Abstract
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.
Abstract (translated)
我们提出了一种通用的算法，用于评估基于单张图像语义分析的姿势估计方法。该算法采用多个高斯过程来表示轻量级假设形状。每个高斯过程（GP）从对象坐标系中多个参考点的距离正常分布到其表面，从而为评分预测姿势提供几何评估框架。我们的置信度度量包括像素反投影平均混合概率到形状模板。在报道的实验中，我们比较了基于GP表示的对象与实际几何模型的准确性，并证明了我们的方法能够捕捉到异质性影响，而不是与分割和姿势估计方法附带的相关内在度量。
URL
https://arxiv.org/abs/2404.16471
