Light field cameras, by harnessing the power of micro-lens array, are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation, a critical aspect of scene interpretation in vision intelligence. However, the extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent vehicles. Besides, inappropriate compression leads to information corruption and data loss. To excavate representative information, we propose a new paradigm, Omni-Aperture Fusion model (OAFuser), which leverages dense context from the central view and discovers the angular information from sub-aperture images to generate a semantically consistent result. To avoid feature loss during network propagation and simultaneously streamline the redundant information from the light field camera, we present a simple yet very effective Sub-Aperture Fusion Module (SAFM) to embed sub-aperture images into angular features without any additional memory cost. Furthermore, to address the mismatched spatial information across viewpoints, we present a Center Angular Rectification Module (CARM) to realize feature resorting and prevent feature occlusion caused by asymmetric information. Our proposed OAFuser achieves state-of-the-art performance on the UrbanLF-Real and -Syn datasets and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +4.53%. The source code of OAFuser will be available at https://github.com/FeiBryantkit/OAFuser.

To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customizable hardware configurations to accelerate light field deep learning research.

In photography, depth of field refers to how much of a three-dimensional space the camera can focus on at once. A shallow depth of field, for example, would keep the subject sharp but blur out much of the foreground and background. Now, researchers at the National Institute of Standards and Technology have taken inspiration from ancient trilobytes to demonstrate a new light field camera with the deepest depth of field ever recorded. Their visual systems were quite complex, including compound eyes, featuring anywhere between tens and thousands of tiny independent units, each with its own cornea, lens and photoreceptor cells. One trilobyte in particular, Dalmanitina socialis, captured the attention of NIST researchers due to its unique compound eye structure.

World's most advanced smartphone uses special sensors to mirror your intelligence & work for you! Tier3D Artificial Intelligence smartphone & smartwatch connect to the Tier3D Artificial Intelligence cloud and contribute to human-like artificial intelligence. Tier3D gadgets crowdsource Artificial Intelligence to generate a deep understanding of what you need to help you in a very precise way and work for you as an intuitive extension of your own mind. For example, if you go to a meeting wearing the Tier3D smartwatch and you promise to send a presentation the next day, the Tier3D watch understands your conversation and automatically generates a presentation highly specific to what you may need.Tier3D AI-Phone comes with an octa-core processor, 6 GB RAM, 128GB memory, and a special 3D light field camera. Tier3D Watch comes with a quad-core processor, 8GB internal memory, and a special 3D light field camera.

In order to fully function in human environments, robot perception will need to account for the uncertainty caused by translucent materials. Translucency poses several open challenges in the form of transparent objects (e.g., drinking glasses), refractive media (e.g., water), and diffuse partial occlusions (e.g., objects behind stained glass panels). This paper presents Plenoptic Monte Carlo Localization (PMCL) as a method for localizing object poses in the presence of translucency using plenoptic (light-field) observations. We propose a new depth descriptor, the Depth Likelihood Volume (DLV), and its use within a Monte Carlo object localization algorithm. We present results of localizing and manipulating objects with translucent materials and objects occluded by layers of translucency. Our PMCL implementation uses observations from a Lytro first generation light field camera to allow a Michigan Progress Fetch robot to perform grasping.