カテゴリレベル物体位置合わせのための全畳み込み幾何特徴
Fully Convolutional Geometric Features for Category-level Object Alignment
同カテゴリの異なる物体インスタンスを正規化座標系に変換し、メトリック学習で全畳み込み幾何特徴を訓練することで、形状の異なるインスタンス間の高精度な位置合わせを実現した。
著者: Qiaojun Feng, Nikolay Atanasov
分類: cs.CV, cs.RO
原文アブストラクト
This paper focuses on pose registration of different object instances from the same category. This is required in online object mapping because object instances detected at test time usually differ from the training instances. Our approach transforms instances of the same category to a normalized canonical coordinate frame and uses metric learning to train fully convolutional geometric features. The resulting model is able to generate pairs of matching points between the instances, allowing category-level registration. Evaluation on both synthetic and real-world data shows that our method provides robust features, leading to accurate alignment of instances with different shapes.
