未見物体の6D姿勢推定：ベンチマークとベースライン
Unseen Object 6D Pose Estimation: A Benchmark and Baselines
訓練時に見ていない新規物体の6D姿勢を推定するタスクを提案し、実画像と合成画像を含むデータセットと新しい評価指標IADDを構築した。2段階のベースライン手法も提案し、その有効性を実験で示した。
著者: Minghao Gou, Haolin Pan, Hao-Shu Fang, Ziyuan Liu, Cewu Lu, Ping Tan
分類: cs.CV, cs.RO
原文アブストラクト
Estimating the 6D pose for unseen objects is in great demand for many real-world applications. However, current state-of-the-art pose estimation methods can only handle objects that are previously trained. In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set. In the mean while, we propose a new metric named Infimum ADD (IADD) which is an invariant measurement for objects with different types of pose ambiguity. A two-stage baseline solution for this task is also provided. By training an end-to-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently. It then calculates the 6D pose from the correspondences using an algorithm robust to object symmetry. Extensive experiments show that our method outperforms several intuitive baselines and thus verify its effectiveness. All the data, code and models will be made publicly available. Project page: www.graspnet.net/unseen6d
