论文标题
主动立体声摄像机和高度反射对象的次数视图预测
Next-Best-View Prediction for Active Stereo Cameras and Highly Reflective Objects
论文作者
论文摘要
使用主动立体声摄像机的深度获取是高度反射对象的一项艰巨任务。设置许可证时，多视图融合可以提供更高的深度完成水平。但是，由于高端主动立体声摄像机的采集速度缓慢，为单个场景收集大量观点通常是不切实际的。在这项工作中，我们提出了一个下一最佳视图框架，以战略性地选择相机观点，以完成反射对象的深度数据。特别是，我们根据phong反射模型和光度响应函数明确对反射表面的镜面反射进行了模拟。鉴于对象CAD模型和灰度图像，我们采用基于RGB的姿势估计器从现有数据中获得当前的姿势预测，该数据用于形成预测的表面正常和深度假设，然后允许我们从随后的框架中评估任何候选观点的信息增益。使用此公式，我们实施了一个主动感知管道，该管道在充满挑战的现实世界数据集上进行了评估。评估结果表明，我们的主动深度采集方法在深度完成和对象姿势估计性能方面优于两个强基础。
Depth acquisition with the active stereo camera is a challenging task for highly reflective objects. When setup permits, multi-view fusion can provide increased levels of depth completion. However, due to the slow acquisition speed of high-end active stereo cameras, collecting a large number of viewpoints for a single scene is generally not practical. In this work, we propose a next-best-view framework to strategically select camera viewpoints for completing depth data on reflective objects. In particular, we explicitly model the specular reflection of reflective surfaces based on the Phong reflection model and a photometric response function. Given the object CAD model and grayscale image, we employ an RGB-based pose estimator to obtain current pose predictions from the existing data, which is used to form predicted surface normal and depth hypotheses, and allows us to then assess the information gain from a subsequent frame for any candidate viewpoint. Using this formulation, we implement an active perception pipeline which is evaluated on a challenging real-world dataset. The evaluation results demonstrate that our active depth acquisition method outperforms two strong baselines for both depth completion and object pose estimation performance.
