论文标题
PixTrack：使用NERF模板和功能 - 金属对齐方式进行精确的6DOF对象姿势跟踪
PixTrack: Precise 6DoF Object Pose Tracking using NeRF Templates and Feature-metric Alignment
论文作者
论文摘要
我们提出了PixTrack，这是一种基于视觉的对象姿势跟踪框架，并使用新型视图合成和深度特征 - 测序。我们遵循基于SFM的重定位范式，在该范式中，我们使用神经辐射场来代表跟踪对象。我们的评估表明，我们的方法会产生高度准确，健壮和无抖动的6DOF姿势姿势估算单眼RGB图像和RGB-D图像中对象的估计，而无需任何数据注释或轨迹平滑。我们的方法还在计算上有效，使得可以通过简单的CPU多处理对多对象跟踪，而无需更改算法。我们的代码可在以下网址找到：https：//github.com/giantai/pixtrack
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
