Abstract: Systems and methods for adjusting an exposure parameter of an imaging device are disclosed. A first exposure level of the imaging device is identified, and a first image of a scene is captured via the imaging device at the first exposure level. The first image of the scene comprises a plurality of polarization images corresponding to different degrees and angles of polarization. Each of the polarization images comprise a plurality of color channels. A gradient for the first image is computed based on the plurality of the polarization images, and a second exposure level is computed based on the gradient. A second image of the scene is captured based on the second exposure level, where the gradient of the second image is greater than a gradient for the first image.

Abstract: A method of performing surface profilometry includes receiving one or more polarization raw frames of a printed layer of a physical object undergoing additive manufacturing, the one or more polarization raw frames being captured at different polarizations by one or more polarization cameras, extracting one or more polarization feature maps in one or more polarization representation spaces from the one or more polarization raw frames, obtaining a coarse layer depth map of the printed layer, generating one or more surface-normal images based on the coarse layer depth map and the one or more polarization feature maps, and generating a 3D reconstruction of the printed layer based on the one or more surface-normal images.

Abstract: Systems and method for expanding a dynamic range associated with an image are disclosed. The method includes capturing an image of a scene via an imaging device using a single exposure. The image of the scene includes a plurality of polarization images corresponding to different angles of polarization, and each of the polarization images comprise a plurality of color channels. The method further includes determining a criterion for each of the plurality of color channels; selecting one color channel of the plurality of color channels based on determining of the criterion; generating a reconstructed image irradiance for the one color channel based on pixels in two or more of the plurality of polarization images obtained for the one color channel; and outputting a reconstructed image with the reconstructed image irradiance.

Abstract: A stereo camera array system includes: a first camera array at a first viewpoint including: a first camera configured to capture images in a first modality, the first modality being viewpoint-independent; and a second camera configured to capture images in a second modality different from the first modality; and a second camera array at a second viewpoint spaced apart along a first baseline from the first camera array at the first viewpoint, the second camera array including: a first camera configured to capture images in the first modality; and a second camera configured to capture images in the second modality.

Abstract: Systems and methods in accordance with embodiments of the invention are disclosed that use super-resolution (SR) processes to use information from a plurality of low resolution (LR) images captured by an array camera to produce a synthesized higher resolution image. One embodiment includes obtaining input images using the plurality of imagers, using a microprocessor to determine an initial estimate of at least a portion of a high resolution image using a plurality of pixels from the input images, and using a microprocessor to determine a high resolution image that when mapped through the forward imaging transformation matches the input images to within at least one predetermined criterion using the initial estimate of at least a portion of the high resolution image.

Abstract: In an embodiment, a 3D facial modeling system includes a plurality of cameras configured to capture images from different viewpoints, a processor, and a memory containing a 3D facial modeling application and parameters defining a face detector, wherein the 3D facial modeling application directs the processor to obtain a plurality of images of a face captured from different viewpoints using the plurality of cameras, locate a face within each of the plurality of images using the face detector, wherein the face detector labels key feature points on the located face within each of the plurality of images, determine disparity between corresponding key feature points of located faces within the plurality of images, and generate a 3D model of the face using the depth of the key feature points.

Abstract: Systems and methods for implementing array cameras configured to perform super-resolution processing to generate higher resolution super-resolved images using a plurality of captured images and lens stack arrays that can be utilized in array cameras are disclosed. An imaging device in accordance with one embodiment of the invention includes at least one imager array, and each imager in the array comprises a plurality of light sensing elements and a lens stack including at least one lens surface, where the lens stack is configured to form an image on the light sensing elements, control circuitry configured to capture images formed on the light sensing elements of each of the imagers, and a super-resolution processing module configured to generate at least one higher resolution super-resolved image using a plurality of the captured images.

Abstract: A method of generating synthetic images of virtual scenes includes: placing, by a synthetic data generator implemented by a processor and memory, three-dimensional (3-D) models of objects in a 3-D virtual scene; adding, by the synthetic data generator, lighting to the 3-D virtual scene, the lighting including one or more illumination sources; applying, by the synthetic data generator, imaging modality-specific materials to the 3-D models of objects in the 3-D virtual scene in accordance with a selected multimodal imaging modality, each of the imaging modality-specific materials including an empirical model; setting a scene background in accordance with the selected multimodal imaging modality; and rendering, by the synthetic data generator, a two-dimensional image of the 3-D virtual scene based on the selected multimodal imaging modality to generate a synthetic image in accordance with the selected multimodal imaging modality.

Abstract: A computer-implemented method for computing a prediction on images of a scene includes: receiving one or more polarization raw frames of a scene, the polarization raw frames being captured with a polarizing filter at a different linear polarization angle; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and computing a prediction regarding one or more optically challenging objects in the scene based on the one or more first tensors in the one or more polarization representation spaces.

Abstract: Embodiments of the invention provide a camera array imaging architecture that computes depth maps for objects within a scene captured by the cameras, and use a near-field sub-array of cameras to compute depth to near-field objects and a far-field sub-array of cameras to compute depth to far-field objects. In particular, a baseline distance between cameras in the near-field subarray is less than a baseline distance between cameras in the far-field sub-array in order to increase the accuracy of the depth map. Some embodiments provide an illumination near-IR light source for use in computing depth maps.

Abstract: A computer-implemented method for surface modeling includes: receiving one or more polarization raw frames of a surface of a physical object, the polarization raw frames being captured with a polarizing filter at different linear polarization angles; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and detecting a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces.

Abstract: A method for estimating a pose of an object includes: receiving a plurality of images of the object captured from multiple viewpoints with respect to the object; initializing a current pose of the object based on computing an initial estimated pose of the object from at least one of the plurality of images; predicting a plurality of 2-D keypoints associated with the object from each of the plurality of images; and computing an updated pose that minimizes a cost function based on a plurality of differences between the 2-D keypoints and a plurality of 3-D keypoints associated with a 3-D model of the object as arranged in accordance with the current pose, and as projected to each of the viewpoints.

Abstract: A computer-implemented method for computing a prediction on images of a scene includes: receiving one or more polarization raw frames of a scene, the polarization raw frames being captured with a polarizing filter at a different linear polarization angle; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and computing a prediction regarding one or more optically challenging objects in the scene based on the one or more first tensors in the one or more polarization representation spaces.

Abstract: A method for estimating a pose of an object includes: receiving a plurality of images of the object captured from multiple viewpoints with respect to the object; initializing a current pose of the object based on computing an initial estimated pose of the object from at least one of the plurality of images; predicting a plurality of 2-D keypoints associated with the object from each of the plurality of images; and computing an updated pose that minimizes a cost function based on a plurality of differences between the 2-D keypoints and a plurality of 3-D keypoints associated with a 3-D model of the object as arranged in accordance with the current pose, and as projected to each of the viewpoints.

Abstract: Systems and methods for adjusting an exposure parameter of an imaging device are disclosed. A first exposure level of the imaging device is identified, and a first image of a scene is captured via the imaging device at the first exposure level. The first image of the scene comprises a plurality of polarization images corresponding to different degrees and angles of polarization. Each of the polarization images comprise a plurality of color channels. A gradient for the first image is computed based on the plurality of the polarization images, and a second exposure level is computed based on the gradient. A second image of the scene is captured based on the second exposure level, where the gradient of the second image is greater than a gradient for the first image.

Abstract: A method for characterizing a pose estimation system includes: receiving, from a pose estimation system, first poses of an arrangement of objects in a first scene; receiving, from the pose estimation system, second poses of the arrangement of objects in a second scene, the second scene being a rigid transformation of the arrangement of objects of the first scene with respect to the pose estimation system; computing a coarse scene transformation between the first scene and the second scene; matching corresponding poses between the first poses and the second poses; computing a refined scene transformation between the first scene and the second scene based on coarse scene transformation, the first poses, and the second poses; transforming the first poses based on the refined scene transformation to compute transformed first poses; and computing an average rotation error and an average translation error of the pose estimation system based on differences between the transformed first poses and the second poses.

Abstract: A computer-implemented method for surface modeling includes: receiving one or more polarization raw frames of a surface of a physical object, the polarization raw frames being captured with a polarizing filter at different linear polarization angles; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and detecting a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces.

Abstract: In an embodiment, a 3D facial modeling system includes a plurality of cameras configured to capture images from different viewpoints, a processor, and a memory containing a 3D facial modeling application and parameters defining a face detector, wherein the 3D facial modeling application directs the processor to obtain a plurality of images of a face captured from different viewpoints using the plurality of cameras, locate a face within each of the plurality of images using the face detector, wherein the face detector labels key feature points on the located face within each of the plurality of images, determine disparity between corresponding key feature points of located faces within the plurality of images, and generate a 3D model of the face using the depth of the key feature points.

Abstract: A method for estimating a pose of an object includes: receiving a plurality of images of the object captured from multiple viewpoints with respect to the object; initializing a current pose of the object based on computing an initial estimated pose of the object from at least one of the plurality of images; predicting a plurality of 2-D keypoints associated with the object from each of the plurality of images; and computing an updated pose that minimizes a cost function based on a plurality of differences between the 2-D keypoints and a plurality of 3-D keypoints associated with a 3-D model of the object as arranged in accordance with the current pose, and as projected to each of the viewpoints.

Abstract: A method for characterizing a pose estimation system includes: receiving, from a pose estimation system, first poses of an arrangement of objects in a first scene; receiving, from the pose estimation system, second poses of the arrangement of objects in a second scene, the second scene being a rigid transformation of the arrangement of objects of the first scene with respect to the pose estimation system; computing a coarse scene transformation between the first scene and the second scene; matching corresponding poses between the first poses and the second poses; computing a refined scene transformation between the first scene and the second scene based on coarse scene transformation, the first poses, and the second poses; transforming the first poses based on the refined scene transformation to compute transformed first poses; and computing an average rotation error and an average translation error of the pose estimation system based on differences between the transformed first poses and the second poses.