Qualcomm Incorporated

Hong Cai

Fatih Murat Porikli

Debasmit Das

Jamie Menjay Lin

Yinhao Zhu

Shubhankar Mangesh Borse

Risheek Garrepalli

Minghua Liu

Shichong Peng

Janarbek Matai

Hyojin Park

Hao Su

Aspects of the present disclosure relate to a novel framework for integrating both semantic and instance contexts for panoptic segmentation. In one example aspect, a method for processing image data includes: processing semantic feature data and instance feature data with a panoptic encoding generator to generate a panoptic encoding; processing the panoptic encoding to generate a panoptic segmentation features; and generating the panoptic segmentation mask based on the panoptic segmentation features.

Panoptic segmentation with panoptic, instance, and semantic relations

Certain aspects of the present disclosure provide techniques for generating fine depth maps for images of a scene based on semantic segmentation and segment-based refinement neural networks. An example method generally includes generating, through a segmentation neural network, a segmentation map based on an image of a scene. The segmentation map generally comprises a map segmenting the scene into a plurality of regions, and each region of the plurality of regions is generally associated with one of a plurality of categories. A first depth map of the scene is generated through a first depth neural network based on a depth measurement of the scene. A second depth map of the scene is generated through a depth refinement neural network based on the segmentation map and the first depth map. One or more actions are taken based on the second depth map of the scene.

Depth map completion in visual content using semantic and three-dimensional information

Systems and techniques are provided for part segmentation. For example, a process for performing part segmentation can include obtaining a three-dimensional capture of an object. The method can include generating one or more two-dimensional images of the object from the three-dimensional capture of the object. The method can further include processing the one or more two-dimensional images of the object to generate at least one two-dimensional bounding box associated with a part of the object. The method can include performing three-dimensional part segmentation of the part of the object based on a three-dimensional point cloud generated from the one or more two-dimensional images of the object and the at least one two-dimensional bounding box and based on semantically labeled super points which are merged into subgroups associated with the part of the object.

Three-dimensional object part segmentation using a machine learning model

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San Diego, CA, United States of America

Hong Cai