We present PixTrack, a vision based object pose tracking framework using
novel view synthesis and deep feature-metric alignment. Our evaluations
demonstrate that our method produces highly accurate, robust, and jitter-free
6DoF pose estimates of objects in RGB 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 method and just using CPU multiprocessing.

PixTrack 是一个基于视觉的对象姿态跟踪框架，使用新颖的视角综合和深度特征度量对齐方法，能够在 RGB 图像中高精度、鲁棒且无抖动地估算物体的 6DoF 姿态，无需任何数据注释或轨迹平滑。此外，该方法计算效率高，易于使用 CPU 多进程实现多物体跟踪。

PixTrack: 使用 NeRF 模板和特征度量对齐进行精确的 6 自由度物体位姿跟踪

PixTrack: Precise 6DoF Object Pose Tracking using NeRF Templates and Feature-metric Alignment

We present a novel multi-attentional convolutional architecture to tackle the
problem of real-time RGB-D 6D object pose tracking of single, known objects.
Such a problem poses multiple challenges originating both from the objects'
nature and their interaction with their environment, which previous approaches
have failed to fully address. The proposed framework encapsulates methods for
background clutter and occlusion handling by integrating multiple parallel soft
spatial attention modules into a multitask Convolutional Neural Network (CNN)
architecture. Moreover, we consider the special geometrical properties of both
the object's 3D model and the pose space, and we use a more sophisticated
approach for data augmentation during training. The provided experimental
results confirm the effectiveness of the proposed multi-attentional
architecture, as it improves the State-of-the-Art (SoA) tracking performance by
an average score of 34.03% for translation and 40.01% for rotation, when tested
on the most complete dataset designed, up to date,for the problem of RGB-D
object tracking.

提出一种新颖的多关注卷积架构，通过整合多个并行的软空间关注模块到多任务 CNN 架构中，来解决实时 RGB-D 6D 对象姿态跟踪问题，并考虑对象 3D 模型和姿态空间的特殊几何属性，使用更复杂的数据增强方法进行训练，实验证明该架构有效性得到了提高。

如何追踪你的龙：用于实时 RGB-D 6-DOF 物体姿态跟踪的多重关注框架

How to track your dragon: A Multi-Attentional Framework for real-time  RGB-D 6-DOF Object Pose Tracking

We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects
using a monocular RGB camera. The key idea is to derive a region-based cost
function using temporally consistent local color histograms. While such
region-based cost functions are commonly optimized using first-order gradient
descent techniques, we systematically derive a Gauss-Newton optimization scheme
which gives rise to drastically faster convergence and highly accurate and
robust tracking performance. We furthermore propose a novel complex dataset
dedicated for the task of monocular object pose tracking and make it publicly
available to the community. To our knowledge, it is the first to address the
common and important scenario in which both the camera as well as the objects
are moving simultaneously in cluttered scenes. In numerous experiments -
including our own proposed dataset - we demonstrate that the proposed
Gauss-Newton approach outperforms existing approaches, in particular in the
presence of cluttered backgrounds, heterogeneous objects and partial
occlusions.

该研究提出了一种用于实时追踪刚体三维物体 6 自由度位姿的算法，采用单目 RGB 相机，利用一种基于区域的代价函数，并通过 Gauss-Newton 优化得到高度准确和鲁棒的跟踪性能，特别是在有杂乱背景，异质对象和局部遮挡的情况下，该算法表现出色。