论文标题
使用光字段删除用于静态场景重建的动态对象
Removing Dynamic Objects for Static Scene Reconstruction using Light Fields
论文作者
论文摘要
人们普遍期望机器人应在包括人，家具和汽车在内的静态和动态实体组成的环境中运行。这些动态环境通过将错误引入前端，对视觉同时定位和映射（SLAM）算法提出挑战。灯场通过捕获场景的更完整的视觉信息来提供一种解决此类问题的可能方法。与单个射线从透视摄像机相反，光场捕获了一束从空间中的点出现的光线，从而使我们能够通过重新聚焦过去的对象来透过动态对象。 在本文中，我们提出了一种在存在动态对象的情况下使用使用线性摄像机阵列获得的光场的静态背景的重新聚焦图像的方法。我们同时使用语义分割来估算静态场景的深度和重新聚焦图像，以在单个时间步骤中检测动态对象。这消除了初始化静态图的需求。该算法是可行的，并在GPU上实现，使我们以接近实时速度执行它。我们证明了我们使用带有五个相机阵列的小型机器人获取的方法对现实世界数据的有效性。
There is a general expectation that robots should operate in environments that consist of static and dynamic entities including people, furniture and automobiles. These dynamic environments pose challenges to visual simultaneous localization and mapping (SLAM) algorithms by introducing errors into the front-end. Light fields provide one possible method for addressing such problems by capturing a more complete visual information of a scene. In contrast to a single ray from a perspective camera, Light Fields capture a bundle of light rays emerging from a single point in space, allowing us to see through dynamic objects by refocusing past them. In this paper we present a method to synthesize a refocused image of the static background in the presence of dynamic objects that uses a light-field acquired with a linear camera array. We simultaneously estimate both the depth and the refocused image of the static scene using semantic segmentation for detecting dynamic objects in a single time step. This eliminates the need for initializing a static map . The algorithm is parallelizable and is implemented on GPU allowing us execute it at close to real time speeds. We demonstrate the effectiveness of our method on real-world data acquired using a small robot with a five camera array.
