In recent years, light field (LF) capture and processing has become an integral part of media production. The richness of information available in LFs has enabled novel applications like post-capture depth-of-field editing, 3D reconstruction, segmentation and matting, saliency detection, object detection and recognition, and mixed reality. The efficacy of such applications depends on certain underlying requirements, which are often ignored. For example, some operations such as noise-reduction, or hyperfan-filtering are only possible if a scene point Lambertian radiator. Some other operations such as the removal of obstacles or looking behind objects are only possible if there is at least one ray capturing the required scene point. Consequently, the ray distribution representing a certain scene point is an important characteristic for evaluating processing possibilities. The primary idea in this paper is to establish a relation between the capturing setup and the rays of the LF. To this end, we discretize the view frustum. Traditionally, a uniform discretization of the view frustum results in voxels that represents a single sample on a regularly spaced, 3-D grid. Instead, we use frustum-shaped voxels (froxels), by using depth and capturing-setup dependent discretization of the view frustum. Based on such discretization, we count the number of rays mapping to the same pixel on the capturing device(s). By means of this count, we propose histograms of ray-counts over the froxels (fristograms). Fristograms can be used as a tool to analyze and reveal interesting aspects of the underlying LF, like the number of rays originating from a scene point and the color distribution of these rays. As an example, we show its ability by significantly reducing the number of rays which enables noise reduction while maintaining the realistic rendering of non-Lambertian or partially occluded regions.
翻译:近年来,光场捕获和处理已成为媒体制作的一个组成部分。 光场捕获和处理成为媒体制作的一个组成部分。 光场的丰富信息在LF中丰富, 使得新应用成为了可能, 比如后拍摄深度编辑、 3D重建、 分割和交配、 突出检测、 物体探测和识别以及混杂的现实。 这种应用的功效取决于某些基本要求, 而这些基本要求往往被忽略。 例如, 一些操作, 如减少噪音或超光谱过滤等, 只有在犯罪现场点 兰伯特 散射器的情况下才可能。 某些其他操作, 如清除障碍或看物体后面的操作, 只有在至少有一台射线捕捉到所需的场点的情况下, 才有可能实现。 因此, 代表某个场点的重要特征是, 代表某个场点的射线分布。 本文的主要想法是建立捕捉的设置与LFrix的射线之间的关系。 为此, 我们分解了这些直径的值数。 传统上, 视觉的分解结果可以代表一个固定空间、 3D电网中的单一样本 。 。 相反, 我们使用直径直径的直路路路路路路路的分布, 向基础, 显示直径直径直径直径直径直径直径直径直径,, 显示的直路路路路路路路路路路路路, 显示的直径直径直径向, 直径向, 显示的直径直径向, 直路路路路路路路路路路路路路路路。