宇宙空間における広深度範囲6D物体姿勢推定
Wide-Depth-Range 6D Object Pose Estimation in Space
大気散乱のない宇宙環境で遠距離から近距離まで対象を捉える単一ステージの階層的エンドツーエンドネットワークを提案し、スケール変動に頑健な6D姿勢推定を実現した。
著者: Yinlin Hu, Sebastien Speierer, Wenzel Jakob, Pascal Fua, Mathieu Salzmann
分類: cs.CV, cs.GR, cs.RO
原文アブストラクト
6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric scattering, allowing objects to be visible from a great distance while complicating illumination conditions. Currently available benchmark datasets do not place a sufficient emphasis on this aspect and mostly depict the target in close proximity. Prior work tackling pose estimation under large scale variations relies on a two-stage approach to first estimate scale, followed by pose estimation on a resized image patch. We instead propose a single-stage hierarchical end-to-end trainable network that is more robust to scale variations. We demonstrate that it outperforms existing approaches not only on images synthesized to resemble images taken in space but also on standard benchmarks.
