Abstract: Described herein is a graphics processor comprising a system interconnect and a graphics processor cluster coupled with the system interconnect. The graphics processor cluster includes circuitry configurable to generate per-frame neural representations of a multi-view video via incremental training and transferal of weights.

Abstract: Described herein is a graphics processor comprising a system interconnect and a graphics processor cluster coupled with the system interconnect. The graphics processor cluster includes circuitry configurable to generate per-frame neural representations of a multi-view video via incremental training and transferal of weights.

Abstract: A 360 panoramic video sequence of an architectural or industrial site includes a plurality of 360 images. 3D poses of the 360 images are determined with respect to one another, and a subset of the images extracted for further processing according to selection criteria. The 3D poses of the extracted images is refined based on determined correspondences of the features in the images, and a 3D point cloud of the site is developed from the extracted images with refined pose estimates. An “as-built” representation of the site captured in the panoramic video sequence is created from the sparse 3D point cloud and aligned to a 2D or 3D plan of the site. Once so aligned, the extracted images may be presented to a user in conjunction with their plan positions (e.g., overlayed on the site plan). Optionally, a point cloud or mesh view of the site may also be returned.

Abstract: Computer vision systems and methods for vehicle damage detection are provided. An embodiment of the system generates a dataset and trains a neural network with a plurality of images of the dataset to learn to detect an attribute of a vehicle present in an image of the dataset and to classify at least one feature of the detected attribute. The system can detect the attribute of the vehicle and classify the at least one feature of the detected attribute by the trained neural network. In addition, an embodiment of the system utilizes a neural network to reconstruct a vehicle from one or more digital images.

Abstract: A 360 panoramic video sequence of an architectural or industrial site includes a plurality of 360 images. 3D poses of the 360 images are determined with respect to one another, and a subset of the images extracted for further processing according to selection criteria. The 3D poses of the extracted images is refined based on determined correspondences of the features in the images, and a 3D point cloud of the site is developed from the extracted images with refined pose estimates. An “as-built” representation of the site captured in the panoramic video sequence is created from the sparse 3D point cloud and aligned to a 2D or 3D plan of the site. Once so aligned, the extracted images may be presented to a user in conjunction with their plan positions (e.g., overlayed on the site plan). Optionally, a point cloud or mesh view of the site may also be returned.

Abstract: A 360 panoramic video sequence of an architectural or industrial site includes a plurality of 360 images. 3D poses of the 360 images are determined with respect to one another, and a subset of the images extracted for further processing according to selection criteria. The 3D poses of the extracted images is refined based on determined correspondences of the features in the images, and a 3D point cloud of the site is developed from the extracted images with refined pose estimates. An “as-built” representation of the site captured in the panoramic video sequence is created from the sparse 3D point cloud and aligned to a 2D or 3D plan of the site. Once so aligned, the extracted images may be presented to a user in conjunction with their plan positions (e.g., overlayed on the site plan). Optionally, a point cloud or mesh view of the site may also be returned.

Abstract: A 360 panoramic video sequence of an architectural or industrial site includes a plurality of 360 images. 3D poses of the 360 images are determined with respect to one another, and a subset of the images extracted for further processing according to selection criteria. The 3D poses of the extracted images is refined based on determined correspondences of the features in the images, and a 3D point cloud of the site is developed from the extracted images with refined pose estimates. An “as-built” representation of the site captured in the panoramic video sequence is created from the sparse 3D point cloud and aligned to a 2D or 3D plan of the site. Once so aligned, the extracted images may be presented to a user in conjunction with their plan positions (e.g., overlayed on the site plan). Optionally, a point cloud or mesh view of the site may also be returned.