An indexing scheme for efficient data-driven verification of 3D pose hypotheses

摘要

We present a novel approach for efficient verification of 3D pose hypotheses using 3D sensory data. This task is generally performed by exhaustively comparing scene and model features at a high computational cost. We avoid exhaustive feature comparison by suing a highly discriminative indexing schemes, which can significantly improve the efficiency of the verification task. The proposed scheme is data-driven, i.e. it is used to determine model features consistent with a given scene feature. For a specific verification task, a number of generic scene features are considered (e.g. surface patch, edge segment).