Abstract: Methods and systems for generating 3D models of surfaces that accurately reconstruct both the global structure of an object and its local features are described. The methods and systems generally operated by fusing point features from the point cloud data with voxel features extracted from voxelization procedures. Furthermore, the methods and systems utilize voxelization at multiple spatial resolutions. The use of point-voxel fusion and multiple spatial resolutions may permit the extraction of both global and local geometric features, increasing the accuracy of 3D modeling of objects.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: A computer-implemented method for detecting landmark pairs in a pair of images is provided. The method includes receiving a pair of images, sampling the pair of images to generate reduced-resolution pairs of images, identifying features in the reduced-resolution pairs of images, matching the features in the image pairs, using the matched features in an increased resolution pair of images as guides for feature matching, and through iteratively guiding feature matching, identifying landmarks in the full-resolution pair of images.

Abstract: A computer-implemented method for detecting landmark pairs in a pair of images is provided. The method includes receiving a pair of images, sampling the pair of images to generate reduced-resolution pairs of images, identifying features in the reduced-resolution pairs of images, matching the features in the image pairs, using the matched features in an increased resolution pair of images as guides for feature matching, and through iteratively guiding feature matching, identifying landmarks in the full-resolution pair of images.