COBRA - COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images
arxiv(2024)
摘要
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.
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