Intrinsic Innovation LLC

Massachusetts Institute of Technology

Boston Polarimetrics, Inc.

Vage Taamazyan

Achuta Kadambi

Kartik Venkataraman

Agastya Kalra

Ramesh Raskar

Supreeth Krishna Rao

Boxin Shi

Ayush Bhandari

Bradley Craig Anderson Brown

Guy Michael Stoppi

James Schiel

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.

Systems and methods for transparent object segmentation using polarization cues

Systems and method for an object from a plurality of objects are disclosed. An image of a scene containing the plurality of objects is obtained, and a segmentation map is generated for the objects in the scene. The shapes of the objects are determined based on the segmentation map. An end effector is adjusted in response to determining the shapes of the objects. The adjusting the end effector includes shaping the end effector according to at least one of the shapes of the objects. The plurality of objects is approached in response to the shaping of the end effector, and one of the plurality of objects is picked with the end effector.

Systems and methods for a vision guided end effector

A polarized event camera system includes: an event camera having a field of view centered around an optical axis, the event camera including an image sensor including a plurality of pixels, each pixel of the event camera operating independently and asynchronously and being configured to generate change events based on changes in intensity of light received by the pixel; and a rotatable linear polarizer aligned with the optical axis of the event camera, the rotatable linear polarizer having a polarization axis, the polarization axis of the rotatable linear polarizer being rotatable about the optical axis of the event camera.

Polarization event cameras

A method for estimating a pose of an object includes: receiving, by a processor, an observed image depicting the object from a viewpoint; computing, by the processor, an instance segmentation map identifying a class of the object depicted in the observed image; loading, by the processor, a 3-D model corresponding to the class of the object; computing, by the processor, a rendered image of the 3-D model in accordance with an initial pose estimate of the object and the viewpoint of the observed image; computing, by the processor, a plurality of dense image-to-object correspondences between the observed image of the object and the 3-D model based on the observed image and the rendered image; and computing, by the processor, the pose of the object based on the dense image-to-object correspondences.

Systems and methods for using computer vision to pick up small objects

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.

Systems and methods for high dynamic range image reconstruction

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.

Systems and methods for pose detection and measurement

A method for estimating a pose of a deformable object includes: receiving, by a processor, a plurality of images depicting the deformable object from multiple viewpoints; computing, by the processor, one or more object-level correspondences and a class of the deformable object depicted in the images; loading, by the processor, a 3-D model corresponding to the class of the deformable object; aligning, by the processor, the 3-D model to the deformable object depicted in the plurality of images to compute a six-degree of freedom (6-DoF) pose of the object; and outputting, by the processor, the 3-D model and the 6-DoF pose of the object.

Systems and methods for six-degree of freedom pose estimation of deformable objects

According to one embodiment of the present disclosure, an imaging system includes: an image sensor including a plurality of subpixels grouped into a plurality of pixels; a polarization system including: a rotatable linear polarizer; and a polarizer mask including a plurality of polarizer filters, the polarizer filters being aligned with corresponding ones of the subpixels, the subpixels of a pixel of the plurality of pixels being located behind polarizer filters at different angles of linear polarization; and imaging optics configured to focus light from a scene onto the image sensor.

Systems and methods for high dynamic range imaging using crossed polarizers

A 3D imaging system uses a depth sensor to produce a coarse depth map, and then uses the coarse depth map as a constraint in order to correct ambiguous surface normals computed from polarization cues. The imaging system outputs an enhanced depth map that has a greater depth resolution than the coarse depth map. The enhanced depth map is also much more accurate than could be obtained from the depth sensor alone. In many cases, the imaging system extracts the polarization cues from three polarized images. Thus, in many implementations, the system takes only three extra images&#x2014;in addition to data used to generate the coarse depth map&#x2014;in order to dramatically enhance the coarse depth map.

Methods and apparatus for enhancing depth maps with polarization cues

In some implementations, scene depth is extracted from dual frequency of a cross-correlation signal. A camera may illuminate a scene with amplitude-modulated light, sweeping the modulation frequency. For each modulation frequency in the sweep, each camera pixel may measure a cross-correlation of incident light and of a reference electrical signal. Each pixel may output a vector of cross-correlation measurements acquired by the pixel during a sweep. A computer may perform an FFT on this vector, identify a dual frequency at the second largest peak in the resulting power spectrum, and calculate scene depth as equal to a fraction, where the numerator is the speed of light times this dual frequency and the denominator is four times pi. In some cases, the two signals being cross-correlated have the same phase as each other during each cross-correlation measurement.

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