Hover Inc.

System and method are provided for scaling a 3-D representation of a building structure. The method includes obtaining images of the building structure, including non-camera anchors. The method also includes identifying reference poses for images based on the non-camera anchors. The method also includes obtaining world map data including real-world poses for the images. The method also includes selecting candidate poses from the real-world poses based on corresponding reference poses. The method also includes calculating a scaling factor for a 3-D representation of the building structure based on correlating the reference poses with the selected candidate poses. Some implementations use structure from motion techniques or LiDAR, in addition to augmented reality frameworks, for scaling the 3-D representations of the building structure. In some implementations, the world map data includes environmental data, such as illumination data, and the method includes generating or displaying the 3-D representation.

Techniques are described for identifying correspondences between images to generate a fundamental matrix for the camera positions related to the images. The resultant fundamental matrix enables epipolar geometry to correlate common features among the images. Correspondences are identified by confirming feature matches across images by applying a homography to data representing features across images. Further techniques are described herein for generating a representation of a boundary of a feature of a structure based on a digital image. In one or more embodiments, generating a representation of a boundary of a particular feature in a digital image comprises determining a portion of the image that corresponds to the structure, and determining a portion of the image that corresponds to the particular feature. One more vanishing points are associated with the portion of the image corresponding to the particular feature. The one or more vanishing points are used to generate a set of bounding lines for the particular feature, based on which the boundary indicator for the feature is generated.

An example provides a method, including: receiving a real-world two-dimensional (2D) image of a scene; obtaining, using a set of one or more processors, a depth map for the real-world 2D image, the depth map being selectively corrected using a second depth map formed using a three-dimensional (3-D) model of the scene; and creating, using the set of one or more processors, a representation of the scene based on the depth map selectively corrected by the 3-D model.

Methods, storage media, and systems for combining disparate 3d models of a common building object are disclosed. Exemplary implementations may: receive a first plurality of images; generate a first 3d model based on the first plurality of images; receive a second plurality of images; generate a second 3d model based on the second plurality of images; and align, in a common 3d coordinate system, the first 3d model with the second 3d model.

A system and method is provided for constructing a labeled and dimensioned multidimensional (e.g., 3D) building model from building object imagery (e.g., ground-level imagery). The method begins by retrieve building object imagery, the building object imagery collected based on directed capture with a mobile device. The method continues by constructing a scaled multi-dimensional building model, the scale based on sizing of at least one selected architectural feature. The method continues by identifying architectural elements within facades of the multi-dimensional building model. The method continues by determining dimensions of at least one of the architectural elements, the dimensions based on the scale. The method continues by determining dimensions (e.g., area) of at least one of the architectural elements. The method continues by labeling each identified architectural element with at least an identifier and by labeling at least one of the architectural elements with the determined dimensions.

System and method are provided for scaling a 3-D representation of a building structure. The method includes obtaining world map data including a first track of real-world poses for a plurality of images. The plurality of images comprises non-camera anchors. The method also includes detecting a discrepancy in at least one real-world pose of the first track. The method also includes in response to detecting a discrepancy, generating a new track of real-world poses. The method also includes calculating a scaling factor for a 3-D representation of the building structure based on sampling across a plurality of tracks. The plurality of tracks comprises at least the first track and the new track.

System and method are provided for scaling a 3-D representation of a building structure. The method includes obtaining images of the building structure, including non-camera anchors. The method also includes identifying reference poses for images based on the non-camera anchors. The method also includes obtaining world map data including real-world poses for the images. The method also includes selecting candidate poses from the real-world poses based on corresponding reference poses. The method also includes calculating a scaling factor for a 3-D representation of the building structure based on correlating the reference poses with the selected candidate poses. Some implementations use structure from motion techniques or LiDAR, in addition to augmented reality frameworks, for scaling the 3-D representations of the building structure. In some implementations, the world map data includes environmental data, such as illumination data, and the method includes generating or displaying the 3-D representation.

Techniques are described for identifying correspondences between images to generate a fundamental matrix for the camera positions related to the images. The resultant fundamental matrix enables epipolar geometry to correlate common features among the images. Correspondences are identified by confirming feature matches across images by applying a homography to data representing features across images. Further techniques are described herein for generating a representation of a boundary of a feature of a structure based on a digital image. In one or more embodiments, generating a representation of a boundary of a particular feature in a digital image comprises determining a portion of the image that corresponds to the structure, and determining a portion of the image that corresponds to the particular feature. One more vanishing points are associated with the portion of the image corresponding to the particular feature. The one or more vanishing points are used to generate a set of bounding lines for the particular feature, based on which the boundary indicator for the feature is generated.

Systems and methods are described for creating three dimensional models of building objects by creating a point cloud from a plurality of input images, defining edges of the building object's surfaces represented by the point cloud, creating simplified geometries of the building object's surfaces and constructing a building model based on the simplified geometries. Input images may include ground, orthographic, or oblique images. The resultant model may be scaled according to correlation with select image types and textured.

Systems and methods are described for creating three dimensional models of building objects by creating a point cloud from a plurality of input images, defining edges of the building object's surfaces represented by the point cloud, creating simplified geometries of the building object's surfaces and constructing a building model based on the simplified geometries. Input images may include ground, orthographic, or oblique images. The resultant model may be scaled according to correlation with select image types and textured.