セグメンテーションと形状予測事前分布によるカテゴリレベル姿勢の確率的推定
Probabilistic Category-Level Pose Estimation via Segmentation and Predicted-Shape Priors
生成物体モデルによる3D形状推定とセグメンテーション情報を統合し、混合密度ネットワークで3DOF姿勢の多峰分布を予測するカテゴリレベル姿勢推定手法を提案した論文。
著者: Benjamin Burchfiel, George Konidaris
分類: cs.CV, cs.RO
原文アブストラクト
We introduce a new method for category-level pose estimation which produces a distribution over predicted poses by integrating 3D shape estimates from a generative object model with segmentation information. Given an input depth-image of an object, our variable-time method uses a mixture density network architecture to produce a multi-modal distribution over 3DOF poses; this distribution is then combined with a prior probability encouraging silhouette agreement between the observed input and predicted object pose. Our approach significantly outperforms the current state-of-the-art in category-level 3DOF pose estimation---which outputs a point estimate and does not explicitly incorporate shape and segmentation information---as measured on the Pix3D and ShapeNet datasets.
