Embodiments described herein provided for an instruction and associated logic to enable a processing resource including a tensor accelerator to perform optimized computation of sparse submatrix operations. One embodiment provides a parallel processor comprising a processing cluster coupled with the cache memory. The processing cluster includes a plurality of multiprocessors coupled with a data interconnect, where a multiprocessor of the plurality of multiprocessors includes a tensor core configured to load tensor data and metadata associated with the tensor data from the cache memory, wherein the metadata indicates a first numerical transform applied to the tensor data, perform an inverse transform of the first numerical transform, perform a tensor operation on the tensor data after the inverse transform is performed, and write output of the tensor operation to a memory coupled with the processing cluster.

Methods for lossy and lossless pre-processing of image data. In one embodiment, a method for lossy pre-processing image data, where the method may include, at a computing device: receiving the image data, where the image data includes a model having a mesh, the mesh includes vertices defining a surface, the vertices including attribute vectors, and the attribute vectors including values. The method also including quantizing the values of the attribute vectors to produce modified values, where a precision of the modified values is determined based on a largest power determined using a largest exponent of the values, encoding pairs of the modified values into two corresponding units of information. The method also including, for each pair of the pairs of the modified values, serially storing the two corresponding units of information as a data stream into a buffer, and compressing the data stream in the buffer.

A transaction card construction and computer-implemented methods for a transaction card are described. The transaction card has vector formatted visible information lasered onto its surface. In some embodiments, systems and methods are disclosed for enabling the sourcing of visible information using a scalable vector format. The systems and methods may receive a request to add a first plurality of visible information to a transaction card and capture an image of the first plurality of visible information. The systems and methods may also map the image to a bounding box and convert the mapped image into vector format. In addition, the systems and methods may provide the converted image to a laser machine.

Disclosed herein is a method for piecewise linear scaling of a geometry atlas, the method including generating min-max normalized depth values and generating geometry atlases by scaling the depth values so as to correspond to the gradients of multiple linear intervals.

The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

An image decoding apparatus obtain pieces of coded data that is included in a bitstream and generated by coding tiles. Tile boundary independence information is further obtained from the bitstream, with the tile boundary independence information indicating whether each of boundaries between the tiles is one of a first boundary or a second boundary. The pieces of coded data are decoded to generate image data of the tiles. Image data of a first tile is generated by decoding a first code string included in first coded data with reference to decoding information of a decoded tile when the tile boundary independence information indicates the first boundary, and by decoding the first code string without referring to the decoding information of the decoded tile when the tile boundary independence information indicates the second boundary.

Methods and apparatus for compressing image data are described along with corresponding methods and apparatus for decompressing the compressed image data. An encoder unit, which generates the compressed image data, comprises an input arranged to receive a first image and a second image, wherein the second image is twice the width and height of the first image, a prediction generator arranged to generate a prediction texture from the first image using an adaptive interpolator, a difference texture generator arranged to generate a difference texture from the prediction texture and the second image and in encoder unit arranged to encode the difference texture.

A transaction card construction and computer-implemented methods for a transaction card are described. The transaction card has vector formatted visible information lasered onto its surface. In some embodiments, systems and methods are disclosed for enabling the sourcing of visible information using a scalable vector format. The systems and methods may receive a request to add a first plurality of visible information to a transaction card and capture an image of the first plurality of visible information. The systems and methods may also map the image to a bounding box and convert the mapped image into vector format. In addition, the systems and methods may provide the converted image to a laser machine.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a floorplan user interface. One of the methods includes receiving a vector image file that depicts a floorplan; for each of multiple zoom levels for the floorplan: generating, using the vector image file, two or more image tiles that each have the same tile size, have a file size that is smaller than a vector image file size for the vector image file, and depict a portion of the floorplan; and providing, to a device, an image tile from the two or more image tiles for presentation on a display.

A machine can be specially configured to generate one or more atlases that include two-dimensional texture maps and their corresponding UV maps from a three-dimensional object, compress the atlases, decompress the atlases, store the atlases, access the atlases, communicate the atlases, apply the texture maps from the atlases to a three-dimensional model, or otherwise process the atlases, the texture maps, the UV maps, or any suitable combination thereof. The atlases, texture maps, UV maps, or any suitable combination thereof can be generated, compiled or otherwise created by the machine in a manner that is computationally efficient to compress and decompress using video compression and decompression techniques.