不確実性推定を伴うRGB動画からのキーポイントベースカテゴリレベル物体姿勢追跡
Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation
単眼RGB動画の連続フレームから既知カテゴリ物体の6DoF姿勢とバウンディング直方体を、キーポイント分布の確率的フィルタリングにより不確実性を考慮して推定・追跡する手法を提案した。
著者: Yunzhi Lin, Jonathan Tremblay, Stephen Tyree, Patricio A. Vela, Stan Birchfield
分類: cs.CV, cs.RO
原文アブストラクト
We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category. Our method takes as input the previous and current frame from a monocular RGB video, as well as predictions from the previous frame, to predict the bounding cuboid and 6-DoF pose (up to scale). Internally, a deep network predicts distributions over object keypoints (vertices of the bounding cuboid) in image coordinates, after which a novel probabilistic filtering process integrates across estimates before computing the final pose using PnP. Our framework allows the system to take previous uncertainties into consideration when predicting the current frame, resulting in predictions that are more accurate and stable than single frame methods. Extensive experiments show that our method outperforms existing approaches on the challenging Objectron benchmark of annotated object videos. We also demonstrate the usability of our work in an augmented reality setting.
