反射鲁棒的基于光场的6DoF物体跟踪
Reflection-Robust 6DoF Object Tracking with Light Fields
- Australian Centre for Robotics, School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney（悉尼大学航空航天、机械与机电工程学院澳大利亚机器人中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出基于光场的反射鲁棒6DoF跟踪器，利用反射环境贴图作为姿态线索，在反射物体上保持精度，优于现有基线。
AI中文摘要：
跟踪运动刚体物体的6DoF姿态是机器人和自动驾驶的基础，但现有的跟踪器假设物体外观在序列中保持稳定，这一假设在反射表面上会失效，因为反射表面的外观会随其镜像环境而变化。我们提出了一种基于光场的反射鲁棒6DoF跟踪器，将这一表面上的干扰转化为姿态线索。每帧中，我们的方法在反射干扰下鲁棒地恢复深度，将其反投影为点云，并估计表面法线。随后，它将物体的视角依赖外观分解为漫反射反照率和其反射的环境贴图，从而得到一个可重新照明的表面光场。从粗略初始化开始，我们通过恢复的环境贴图对其重新照明，并在光度损失上优化姿态。由于运动物体镜像场景的新部分，环境贴图随序列推进而填充，随时间增强这一信号。为评估该方法，我们引入了一个光场跟踪数据集，该数据集从机器人操作基准重新渲染，包含四个受控反射率水平，每个水平配以模拟深度，重现消费级RGB-D传感器在光滑表面上的失效方式。此外，我们在两个捕获的光场序列上进行了评估。我们的方法在漫反射物体上落后于最强基线，但它是唯一在完全反射物体上保持精度的，而所有基线在此均退化。
英文摘要：
Tracking the 6DoF pose of a moving rigid object is fundamental to robotics and autonomous driving, but existing trackers assume that object appearance is stable across a sequence, an assumption that breaks down on reflective surfaces whose appearance changes as they mirror the environment. We introduce a light field based reflection-robust 6DoF tracker that turns this apparent nuisance into a pose cue. Per frame, our method recovers depth robustly against reflections, back-projects it into a point cloud, and estimates surface normals. It then decomposes the object's view-dependent appearance into a diffuse albedo and the environment map it reflects, resulting in a relightable surface light field. Starting from a coarse initialization, we relight it by the recovered environment map and optimize the pose on the photometric loss. Because a moving object mirrors new parts of the scene, the environment map fills in as the sequence proceeds, sharpening this signal over time. To evaluate this approach, we introduce a light field tracking dataset re-rendered from a robotic manipulation benchmark at four controlled reflectivity levels, each paired with simulated depth that reproduces how consumer RGB-D sensors fail on shiny surfaces. Additionally, we evaluate on two captured light field sequences. Our method trails the strongest baselines on diffuse objects and is the only one that holds its accuracy on fully reflective objects, where every baseline degrades.
