One exemplary implementation facilitates object detection using multiple scans of an object in different lighting conditions. For example, a first scan of the object can be created by capturing images of the object by moving an image sensor on a first path in a first lighting condition, e.g., bright lighting. A second scan of the object can then be created by capturing additional images of the object by moving the image sensor on a second path in a second lighting condition, e.g., dim lighting. Implementations determine a transform that associates the scan data from these multiple scans to one another and use the transforms to generate a 3D model of the object in a single coordinate system. Augmented content can be positioned relative to that object in the single coordinate system and thus will be displayed in the appropriate location regardless of the lighting condition in which the physical object is later detected.

One exemplary implementation facilitates object detection using multiple scans of an object in different lighting conditions. For example, a first scan of the object can be created by capturing images of the object by moving an image sensor on a first path in a first lighting condition, e.g., bright lighting. A second scan of the object can then be created by capturing additional images of the object by moving the image sensor on a second path in a second lighting condition, e.g., dim lighting. Implementations determine a transform that associates the scan data from these multiple scans to one another and use the transforms to generate a 3D model of the object in a single coordinate system. Augmented content can be positioned relative to that object in the single coordinate system and thus will be displayed in the appropriate location regardless of the lighting condition in which the physical object is later detected.

Some embodiments are directed to a segmentation of medical images. For example, a medical image may be registering to multiple atlas images after which a segmentation function may be applied. Multiple segmentation may be fused into a final overall segmentation. The atlas images may be selected on the basis of high segmentation quality or low registration quality.

Some embodiments are directed to a segmentation of medical images. For example, a medical image may be registering to multiple atlas images after which a segmentation function may be applied. Multiple segmentation may be fused into a final overall segmentation. The atlas images may be selected on the basis of high segmentation quality or low registration quality.

A method allows an estimator or other party to “Walk the Drawings.” An estimator can open one or more sets of drawings on a mobile device and walk those same electronic drawings as they actually walk or traverse the physical site itself. In other words, as the estimator physically moves across the construction site in the real world, their electronic icon (avatar) moves across the corresponding electronic drawings on their mobile device. Now the estimator can identify present and future such as features that need to be accessible being buried under asphalt. While walking the drawings, the estimator can label any challenges or features by simply clicking their avatar and it will post that geo-stamped location complete with corresponding notes and photos straight to the drawings for later review and analysis.

A method allows an estimator or other party to “Walk the Drawings.” An estimator can open one or more sets of drawings on a mobile device and walk those same electronic drawings as they actually walk or traverse the physical site itself. In other words, as the estimator physically moves across the construction site in the real world, their electronic icon (avatar) moves across the corresponding electronic drawings on their mobile device. Now the estimator can identify present and future such as features that need to be accessible being buried under asphalt. While walking the drawings, the estimator can label any challenges or features by simply clicking their avatar and it will post that geo-stamped location complete with corresponding notes and photos straight to the drawings for later review and analysis.

A method for calibrating a multiplicity of cameras on a vehicle in a common coordinate system. The method includes: a) acquiring camera images using each camera of the multiplicity of cameras, b) ascertaining overlap regions of camera images acquired in step a), c) setting up a common optimization function, which describes the alignments of each camera of the multiplicity of cameras as a target variable of the optimization, based on the overlap regions, d) solving the common optimization function set up in step c) to ascertain the alignments of each camera.

A method for calibrating a multiplicity of cameras on a vehicle in a common coordinate system. The method includes: a) acquiring camera images using each camera of the multiplicity of cameras, b) ascertaining overlap regions of camera images acquired in step a), c) setting up a common optimization function, which describes the alignments of each camera of the multiplicity of cameras as a target variable of the optimization, based on the overlap regions, d) solving the common optimization function set up in step c) to ascertain the alignments of each camera.

The present disclosure relates to systems, devices, and methods to augment a two-dimensional image.

The present disclosure relates to systems, devices, and methods to augment a two-dimensional image.