PixTrack: NeRFテンプレートと特徴量メトリック位置合わせによる高精度6DoF物体姿勢追跡
PixTrack: Precise 6DoF Object Pose Tracking using NeRF Templates and Feature-metric Alignment
NeRFで物体を正準表現し、深層特徴量メトリックで位置合わせすることで、アノテーションや軌道平滑化なしに単眼RGB/RGB-D画像から高精度でジッタのない6DoF姿勢追跡を実現した。
著者: Prajwal Chidananda, Saurabh Nair, Douglas Lee, Adrian Kaehler
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
We present PixTrack, a vision based object pose tracking framework using novel view synthesis and deep feature-metric alignment. We follow an SfM-based relocalization paradigm where we use a Neural Radiance Field to canonically represent the tracked object. Our evaluations demonstrate that our method produces highly accurate, robust, and jitter-free 6DoF pose estimates of objects in both monocular RGB images and RGB-D images without the need of any data annotation or trajectory smoothing. Our method is also computationally efficient making it easy to have multi-object tracking with no alteration to our algorithm through simple CPU multiprocessing. Our code is available at: https://github.com/GiantAI/pixtrack
