Abstract: Methods, storage media, and systems for three-dimensional building model generation based on classification of image elements. An example method includes obtaining images depicting a building, with individual images being taken at individual positions about an exterior of the building, and with the images being associated with camera properties reflecting extrinsic and/or intrinsic camera parameters. Semantic labels are determined for the images via a machine learning model, with the labels being associated with elements of the building, and with the semantic labels being associated with two-dimensional positions in the images. Three-dimensional positions associated with the plurality of elements are estimated, with estimating being based on one or more epipolar constraints. A three-dimensional representation of at least a portion of the building is generated, with the portion including a roof of the building.

Abstract: System and methods for generating similar successive predictions to a three-dimensional scene. An example method includes accessing a three-dimensional model associated with a building, the three-dimensional model being associated with geometry which is included in a scene; generating, via one or more machine learning models, a first viewpoint of the scene from a first viewpoint angle, the first viewpoint including textures applied to the three-dimensional model from the first viewpoint angle and additional image content generated based on at least one parameter; extracting, via one or more machine learning models, updated geometry for inclusion in the scene, wherein the updated geometry includes geometry associated with the additional image content at the first viewpoint angle; and generating, via one or more machine learning models, a second viewpoint of the scene from a second viewpoint angle, where generating the second viewpoint is based on the updated geometry.

Abstract: Methods, storage media, and systems for three-dimensional building model generation based on classification of image elements. An example method includes obtaining images depicting a building, with individual images being taken at individual positions about an exterior of the building, and with the images being associated with camera properties reflecting extrinsic and/or intrinsic camera parameters. Semantic labels are determined for the images via a machine learning model, with the labels being associated with elements of the building, and with the semantic labels being associated with two-dimensional positions in the images. Three-dimensional positions associated with the plurality of elements are estimated, with estimating being based on one or more epipolar constraints. A three-dimensional representation of at least a portion of the building is generated, with the portion including a roof of the building.