2視点6D物体姿勢推定に向けて：融合戦略の比較研究
Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy
2枚のRGB画像から暗黙的な3D情報を学習し、2D特徴と融合することで6D物体姿勢推定を行う枠組みを提案し、Early/Mid/Lateの融合戦略を比較してMid-Fusionが最良であることを示した。
著者: Jun Wu, Lilu Liu, Yue Wang, Rong Xiong
分類: cs.CV, cs.RO
原文アブストラクト
Current RGB-based 6D object pose estimation methods have achieved noticeable performance on datasets and real world applications. However, predicting 6D pose from single 2D image features is susceptible to disturbance from changing of environment and textureless or resemblant object surfaces. Hence, RGB-based methods generally achieve less competitive results than RGBD-based methods, which deploy both image features and 3D structure features. To narrow down this performance gap, this paper proposes a framework for 6D object pose estimation that learns implicit 3D information from 2 RGB images. Combining the learned 3D information and 2D image features, we establish more stable correspondence between the scene and the object models. To seek for the methods best utilizing 3D information from RGB inputs, we conduct an investigation on three different approaches, including Early- Fusion, Mid-Fusion, and Late-Fusion. We ascertain the Mid- Fusion approach is the best approach to restore the most precise 3D keypoints useful for object pose estimation. The experiments show that our method outperforms state-of-the-art RGB-based methods, and achieves comparable results with RGBD-based methods.
