未見物体の3D姿勢推定のための局所類似度融合による検索ベース手法
Fusing Local Similarities for Retrieval-based 3D Orientation Estimation of Unseen Objects
単眼画像から学習時に見ていない物体の3D姿勢を推定するため、クエリ画像と合成参照画像のマルチスケール局所類似度を適応的に融合する検索ベース手法を提案し、未見物体への汎化性能を向上させた。
著者: Chen Zhao, Yinlin Hu, Mathieu Salzmann
分類: cs.CV, cs.RO
原文アブストラクト
In this paper, we tackle the task of estimating the 3D orientation of previously-unseen objects from monocular images. This task contrasts with the one considered by most existing deep learning methods which typically assume that the testing objects have been observed during training. To handle the unseen objects, we follow a retrieval-based strategy and prevent the network from learning object-specific features by computing multi-scale local similarities between the query image and synthetically-generated reference images. We then introduce an adaptive fusion module that robustly aggregates the local similarities into a global similarity score of pairwise images. Furthermore, we speed up the retrieval process by developing a fast retrieval strategy. Our experiments on the LineMOD, LineMOD-Occluded, and T-LESS datasets show that our method yields a significantly better generalization to unseen objects than previous works. Our code and pre-trained models are available at https://sailor-z.github.io/projects/Unseen_Object_Pose.html.
