Abstract: Aligning a source and target model includes calculating a shape descriptor for a plurality of edge points, grouping the edge points by shape descriptor and selecting representative points for each group. Target and source point pair features (PPFs) are calculated between pairs of representative points on the target and source models, where PPFs defines the relative position and orientation of point pairs. Target PPFs are matched with each source PPF and the point pairs associated with the matched PPFs are transformed to align the location and edge direction of a target PPF point with the location and edge direction of a source PPF point. An angle is determined to align the second target PPF point with the second source PPF point, and a modal angle is found among the determined rotation angles. An output transformation is calculated using the transformations associated with the modal angle.

Abstract: The present disclosure relates to methods, devices, and systems for selecting a candidate six dimensional pose hypothesis from among a plurality of six dimensional pose hypotheses. For example, the systems, devices, and methods described herein may be used to quickly, accurately, and precisely select a candidate six dimensional pose hypothesis from among a plurality of six dimensional pose hypotheses so that the selected candidate six dimensional pose hypothesis substantially overlaps with an image of an object to be identified from an image of a plurality of objects. In this manner, an object can be identified from among a plurality of objects based on the selected candidate six dimensional pose hypothesis.

Abstract: This disclosure relates to improved techniques for performing human parsing functions using neural network architectures. The neural network architecture can model human objects in images using a hierarchal graph of interconnected nodes that correspond to anatomical features at various levels. Multi-level inference information can be generated for each of the nodes using separate inference processes. The multi-level inference information for each node can be combined or fused to generate final predictions for each of the nodes. Parsing results may be generated based on the final predictions.

Abstract: The present disclosure relates to methods, devices, and systems for selecting a candidate six dimensional pose hypothesis from among a plurality of six dimensional pose hypotheses. For example, the systems, devices, and methods described herein may be used to quickly, accurately, and precisely select a candidate six dimensional pose hypothesis from among a plurality of six dimensional pose hypotheses so that the selected candidate six dimensional pose hypothesis substantially overlaps with an image of an object to be identified from an image of a plurality of objects. In this manner, an object can be identified from among a plurality of objects based on the selected candidate six dimensional pose hypothesis.

Abstract: This disclosure relates to improved techniques for performing human parsing functions using neural network architectures. The neural network architecture can model human objects in images using a hierarchal graph of interconnected nodes that correspond to anatomical features at various levels. Multi-level inference information can be generated for each of the nodes using separate inference processes. The multi-level inference information for each node can be combined or fused to generate final predictions for each of the nodes. Parsing results may be generated based on the final predictions.

Abstract: This disclosure relates to improved techniques for performing image segmentation functions using neural network architectures. The neural network architecture can include an attentive graph neural network (AGNN) that facilitates performance of unsupervised video object segmentation (UVOS) functions and image object co-segmentation (IOCS) functions. The AGNN can generate a graph that utilizes nodes to represent images (e.g., video frames) and edges to represent relations between the images. A message passing function can propagate messages among the nodes to capture high-order relationship information among the images, thus providing a more global view of the video or image content. The high-order relationship information can be utilized to more accurately perform UVOS and/or IOCS functions.

Abstract: This disclosure relates to improved techniques for performing computer vision functions including motion deblurring functions. The techniques described herein utilize a neural network architecture to perform these functions. The neural network architecture can include a human-aware attention model that is able to distinguish between foreground human objects and background portions of degraded images affected by motion blur. The neural network architecture further includes an encoder-decoder network that separately performs motion deblurring functions on foreground and background portions of degraded images, and reconstructs enhanced images corresponding to the degraded images.