Disclosed herein is a system and method for facilitating estimation of a spatial profile of an environment based on a light detection and ranging (LiDAR) based technique. In one arrangement, the present disclosure facilitates spatial profile estimation based on directing light over one dimension, such as along the vertical direction. In another arrangement, by further directing the one-dimensionally directed light in another dimension, such as along the horizontal direction, the present disclosure facilitates spatial profile estimation based on directing light in two dimensions.

A luminaire including: at least one light source (2), and an optical system (10, 11, 12a, 12b) for directing and/or distributing the light (5) emitted by the source(s) (2) into a desired output light distribution pattern (7); wherein the optical system comprises one or more optical elements (10, 11, 12a, 12b), the or each said optical element (10, 11, 12a, 12b) comprising a thin foil or sheet substrate having at least one optically functional surface or surface layer thereon or on a portion thereof, and wherein: (i) at least a portion of the at least one optically functional surface or surface layer on the substrate of at least one of the one or more optical elements (10, 11, 12a, 12b) has an at least partially diffractive optical function, and/or (ii) at least a portion of the at least one of the one or more optical elements (10, 11, 12a, 12b) is shaped such that its substrate is configured so as to have a non-flat or non-planar shape in three dimensions.

An all-optical Diffractive Deep Neural Network (D.sup.2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D.sup.2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D.sup.2NNs. In alternative embodiments, the all-optical D.sup.2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

Display methods and apparatus are described. In some embodiments, to generate an image, light is selectively emitted from one or more light-emitting elements (such as a μLEDs) in a light-emitting layer. The emitted light from each element is collimated using, for example, an array of microlenses having small apertures. Each beam of collimated light is split by a first diffractive grating into a first generation of child beams, and the first generation of child beams is split by a second diffractive grating into a second generation of child beams. Beams in the second generation of child beams that are not parallel to the original beam of collimated light may be blocked by a spatial light modulator (e.g. an LCD panel). The un-blocked beams operate in some respects as if they had been generated using optics with an aperture larger than the apertures of the microlenses.

Disclosed are a diffractive optical assembly, a laser projection unit, and a depth camera. The diffractive optical assembly includes a sealing assembly and a diffractive optical element. The sealing assembly includes a light transparent first sealing plate, a light transparent second sealing plate, and a spacer. The first sealing plate and the second sealing plate are arranged opposite to each other. The spacers spaces the first sealing plate and the second sealing plate apart. The first sealing plate, the second sealing plate and the spacer cooperatively defines a closed receiving cavity. The diffractive optical element is accommodated in the receiving cavity. The diffractive optical element includes a light transparent diffractive body and a diffractive structure formed on the diffractive body.

An optical module includes first and second transparent substrates and a spacer between the first and second transparent substrates, holding the first transparent substrate in proximity to the second transparent substrate, with first and second diffractive optical elements (DOEs) on respective faces of the first and second transparent substrates. At least first and second capacitance electrodes are disposed respectively on the first and second transparent substrates in proximity to the first and second DOEs. Circuitry is coupled to measure changes in a capacitance between at least the first and second capacitance electrodes.

An eyepiece for projecting an image to an eye of a viewer includes a first planar waveguide positioned in a first lateral plane, a second planar waveguide positioned in a second lateral plane adjacent the first lateral plane, and a third planar waveguide positioned in a third lateral plane adjacent the second lateral plane. The first planar waveguide includes a first diffractive optical element (DOE) coupled thereto and disposed at a first lateral position. The second planar waveguide includes a second DOE coupled thereto and disposed at a second lateral position. The third planar waveguide includes a third DOE coupled thereto and disposed at the second lateral position. The eyepiece further includes an optical filter positioned between the second planar waveguide and the third planar waveguide at the second lateral position.

In some embodiments, a head-mounted, near-eye display system comprises a stack of waveguides having integral spacers separating the waveguides. The waveguides may each include diffractive optical elements that are formed simultaneously with the spacers by imprinting. The spacers are disposed on one major surface of each of the waveguides and indentations are provided on an opposite major surface of each of the waveguides. The indentations are sized and positioned to align with the spacers, thereby forming a self-aligned stack of waveguides. Tops of the spacers may be provided with light scattering features, anti-reflective coatings, and/or light absorbing adhesive to prevent light leakage between the waveguides. As seen in a top-down view, the spacers may be elongated along the same axis as the diffractive optical elements. The waveguides may include structures (e.g., layers of light absorbing materials, rough surfaces, light out-coupling optical elements, and/or light trapping microstructures) along their edges to mitigate reflections and improve the display contrast.

An all-optical Diffractive Deep Neural Network (D.sup.2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D.sup.2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D.sup.2NNs. In alternative embodiments, the all-optical D.sup.2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

A system includes: a light source; first and second gratings; and at least one reflective component that in a first position forms a first light path originating at the light source and extending to the first grating and thereafter to a subsequent component in the system, and that in a second position forms a second light path originating at the light source and extending to the second grating and thereafter to the subsequent component.