Lightweight all-focused light field rendering

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computer Vision and Image Understanding Pub Date : 2024-04-27 DOI:10.1016/j.cviu.2024.104031
Tomáš Chlubna , Tomáš Milet , Pavel Zemčík
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Abstract

This paper proposes a novel real-time method for high-quality view interpolation from light field. The proposal is a lightweight method, which can be used with consumer GPU, reaching same or better quality than existing methods, in a shorter time, with significantly smaller memory requirements. Light field belongs to image-based rendering methods that can produce realistic images without computationally demanding algorithms. The novel view is synthesized from multiple input images of the same scene, captured at different camera positions. Standard rendering techniques, such as rasterization or ray-tracing, are limited in terms of quality, memory footprint, and speed. Light field rendering methods often produce unwanted artifacts resembling ghosting or blur in certain parts of the scene due to unknown geometry of the scene. The proposed method estimates the geometry for each pixel as an optimal focusing distance to mitigate the artifacts. The focusing distance determines which pixels from the input images are mixed to produce the final view. State-of-the-art methods use a constant-step pixel matching scan that iterates over a range of focusing distances. The scan searches for a distance with the smallest color dispersion of the contributing pixels, assuming that they belong to the same spot in the scene. The paper proposes an optimal scanning strategy of the focusing range, an improved color dispersion metric, and other minor improvements, such as sampling block size adjustment, out-of-bounds sampling, and filtering. Experimental results show that the proposal uses less resources, achieves better visual quality, and is significantly faster than existing light field rendering methods. The proposal is 8× faster than the methods in the same category. The proposal uses only four closest views from the light field data and reduces the necessary data transfer. Existing methods often require the full light field grid, which is typically 8 × 8 images large. Additionally, a new 4K light field dataset, containing scenes of various types, was created and published. An optimal novel method for light field acquisition is also proposed and used to create the dataset.

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轻量级全聚焦光场渲染
本文提出了一种从光场进行高质量视图插值的新型实时方法。该建议是一种轻量级方法,可与消费级 GPU 配合使用,在更短的时间内达到与现有方法相同或更高的质量,内存需求也大大降低。光场属于基于图像的渲染方法,无需高计算要求的算法就能生成逼真的图像。新颖的视图是由同一场景的多幅输入图像合成的,这些图像是在不同的摄像机位置拍摄的。光栅化或光线追踪等标准渲染技术在质量、内存占用和速度方面都受到限制。由于场景的几何形状未知,光场渲染方法通常会在场景的某些部分产生类似鬼影或模糊的不想要的伪影。所提出的方法将每个像素的几何形状估算为最佳聚焦距离,以减少伪影。对焦距离决定了将输入图像中的哪些像素进行混合,以生成最终视图。最先进的方法使用恒定步长的像素匹配扫描,在一系列对焦距离中进行迭代。扫描时,假设输入像素属于场景中的同一个点,则会寻找一个像素颜色离散度最小的距离。本文提出了聚焦范围的最佳扫描策略、改进的色彩色散度量,以及其他一些小的改进,如采样块大小调整、界外采样和过滤。实验结果表明,与现有的光场渲染方法相比,该建议使用了更少的资源,实现了更好的视觉质量,而且速度明显更快。该方案比同类方法快 8 倍。该方案只使用光场数据中的四个最近视图,减少了必要的数据传输。现有方法通常需要完整的光场网格,而这通常需要 8 × 8 幅图像。此外,还创建并发布了一个新的 4K 光场数据集,其中包含各种类型的场景。此外,还提出了一种最佳的光场采集新方法,并用于创建数据集。
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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