単一参照視点からの汎化可能な相対物体姿勢推定のための3D認識仮説検証
3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation
物体の単一参照画像と異なる姿勢のクエリ画像から相対姿勢を推定するため、複数の姿勢仮説を生成し3D変換を適用して検証するフレームワークを提案。未見物体や大きな姿勢変化に頑健。
著者: Chen Zhao, Tong Zhang, Mathieu Salzmann
分類: cs.CV, cs.RO
原文アブストラクト
Prior methods that tackle the problem of generalizable object pose estimation highly rely on having dense views of the unseen object. By contrast, we address the scenario where only a single reference view of the object is available. Our goal then is to estimate the relative object pose between this reference view and a query image that depicts the object in a different pose. In this scenario, robust generalization is imperative due to the presence of unseen objects during testing and the large-scale object pose variation between the reference and the query. To this end, we present a new hypothesis-and-verification framework, in which we generate and evaluate multiple pose hypotheses, ultimately selecting the most reliable one as the relative object pose. To measure reliability, we introduce a 3D-aware verification that explicitly applies 3D transformations to the 3D object representations learned from the two input images. Our comprehensive experiments on the Objaverse, LINEMOD, and CO3D datasets evidence the superior accuracy of our approach in relative pose estimation and its robustness in large-scale pose variations, when dealing with unseen objects.
