Topic
Light field
About: Light field is a research topic. Over the lifetime, 5357 publications have been published within this topic receiving 87424 citations.
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TL;DR: In this article, the full control of the optical radiation pressure at fixed photon flux and incident angle by the photon spin was reported by using transparent chiral liquid crystal droplets that enable a strong coupling between the linear and angular degrees of freedom of a light field.
Abstract: We report on the full control of the optical radiation pressure at fixed photon flux and incident angle by the photon spin. This is done by using transparent chiral liquid crystal droplets that enable a strong coupling between the linear and angular degrees of freedom of a light field. From these results, we anticipate optical sorting of particles with different chirality as well as novel optical trapping and micromanipulation strategies.
54 citations
01 Aug 2008
TL;DR: In this article, the authors present different designs of multi-dimensional displays which passively react to the light of the environment behind, and combine multiple of these devices to build a display that renders a 6D experience, where the incident 2D illumination influences the outgoing light field, both in the spatial and in the angular domain.
Abstract: Traditional flat screen displays present 2D images. 3D and 4D displays have been proposed making use of lenslet arrays to shape a fixed outgoing light field for horizontal or bidirectional parallax. In this article, we present different designs of multi-dimensional displays which passively react to the light of the environment behind. The prototypes physically implement a reflectance field and generate different light fields depending on the incident illumination, for example light falling through a window. We discretize the incident light field using an optical system, and modulate it with a 2D pattern, creating a flat display which is view and illumination-dependent. It is free from electronic components. For distant light and a fixed observer position, we demonstrate a passive optical configuration which directly renders a 4D reflectance field in the real-world illumination behind it. We further propose an optical setup that allows for projecting out different angular distributions depending on the incident light direction. Combining multiple of these devices we build a display that renders a 6D experience, where the incident 2D illumination influences the outgoing light field, both in the spatial and in the angular domain. Possible applications of this technology are time-dependent displays driven by sunlight, object virtualization and programmable light benders / ray blockers without moving parts.
54 citations
23 Jun 2013
TL;DR: This work presents a novel computational imaging solution by exploiting the light field probe (LF-Probe), which can reliably reconstruct small to medium scale gas flows and shows that the use of ray-ray correspondences can greatly improve the reconstruction.
Abstract: Transparent gas flows are difficult to reconstruct: the refractive index field (RIF) within the gas volume is uneven and rapidly evolving, and correspondence matching under distortions is challenging. We present a novel computational imaging solution by exploiting the light field probe (LF-Probe). A LF-probe resembles a view-dependent pattern where each pixel on the pattern maps to a unique ray. By observing the LF-probe through the gas flow, we acquire a dense set of ray-ray correspondences and then reconstruct their light paths. To recover the RIF, we use Fermat's Principle to correlate each light path with the RIF via a Partial Differential Equation (PDE). We then develop an iterative optimization scheme to solve for all light-path PDEs in conjunction. Specifically, we initialize the light paths by fitting Hermite splines to ray-ray correspondences, discretize their PDEs onto voxels, and solve a large, over-determined PDE system for the RIF. The RIF can then be used to refine the light paths. Finally, we alternate the RIF and light-path estimations to improve the reconstruction. Experiments on synthetic and real data show that our approach can reliably reconstruct small to medium scale gas flows. In particular, when the flow is acquired by a small number of cameras, the use of ray-ray correspondences can greatly improve the reconstruction.
54 citations
25 Jul 2018
TL;DR: Deep surface light field (DSF) as discussed by the authors is a neural network based technique that uses only moderate sampling for high fidelity rendering, which automatically fills in the missing data by leveraging different sampling patterns across the vertices and at the same time eliminates redundancies due to the network's prediction capability.
Abstract: A surface light field represents the radiance of rays originating from any points on the surface in any directions. Traditional approaches require ultra-dense sampling to ensure the rendering quality. In this paper, we present a novel neural network based technique called deep surface light field or DSLF to use only moderate sampling for high fidelity rendering. DSLF automatically fills in the missing data by leveraging different sampling patterns across the vertices and at the same time eliminates redundancies due to the network's prediction capability. For real data, we address the image registration problem as well as conduct texture-aware remeshing for aligning texture edges with vertices to avoid blurring. Comprehensive experiments show that DSLF can further achieve high data compression ratio while facilitating real-time rendering on the GPU.
54 citations
TL;DR: A new Light Field representation for efficient Light Field processing and rendering called Fourier Disparity Layers, which allows real-time Light Field rendering and direct applications such as view interpolation or extrapolation and denoising are presented and evaluated.
Abstract: In this paper, we present a new Light Field representation for efficient Light Field processing and rendering called Fourier Disparity Layers (FDL). The proposed FDL representation samples the Light Field in the depth (or equivalently the disparity) dimension by decomposing the scene as a discrete sum of layers. The layers can be constructed from various types of Light Field inputs, including a set of sub-aperture images, a focal stack, or even a combination of both. From our derivations in the Fourier domain, the layers are simply obtained by a regularized least square regression performed independently at each spatial frequency, which is efficiently parallelized in a GPU implementation. Our model is also used to derive a gradient descent-based calibration step that estimates the input view positions and an optimal set of disparity values required for the layer construction. Once the layers are known, they can be simply shifted and filtered to produce different viewpoints of the scene while controlling the focus and simulating a camera aperture of arbitrary shape and size. Our implementation in the Fourier domain allows real-time Light Field rendering. Finally, direct applications such as view interpolation or extrapolation and denoising are presented and evaluated.
54 citations