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公开(公告)号:US20240161484A1
公开(公告)日:2024-05-16
申请号:US18418386
申请日:2024-01-22
Applicant: SHANGHAITECH UNIVERSITY
Inventor: Peihao WANG , Jiakai ZHANG , Xinhang LIU , Zhijie LIU , Jingyi YU
CPC classification number: G06V10/82 , G06T7/37 , G06T2207/20084
Abstract: A computer-implemented method is provided. The method includes obtaining a plurality of images representing projections of an object placed in a plurality of poses and a plurality of translations; assigning a pose embedding vector, a flow embedding vector and a contrast transfer function (CTF) embedding vector to each image; encoding, by a computer device, a machine learning model comprising a pose network, a flow network, a density network and a CTF network; training the machine learning model using the plurality of images; and reconstructing a 3D structure of the object based on the trained machine learning module.
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公开(公告)号:US20240412377A1
公开(公告)日:2024-12-12
申请号:US18542803
申请日:2023-12-18
Applicant: SHANGHAITECH UNIVERSITY
Inventor: Peihao WANG , Jiakai ZHANG , Xinhang LIU , Zhijie LIU , Jingyi YU
Abstract: Described herein are methods and non-transitory computer-readable media of a computing system configured to obtain a plurality of images of an object from a plurality of orientations at a plurality of times. A machine learning model is encoded to represent a continuous density field of the object that maps a spatial coordinate to a density value. The machine learning model comprises a deformation module configured to deform the spatial coordinate in accordance with a timestamp and a trained deformation weight. The machine learning model further comprises a neural radiance module configured to derive the density value in accordance with the deformed spatial coordinate, the timestamp, a direction, and a trained radiance weight. The machine learning model is trained using the plurality of images. A three-dimensional structure of the object is constructed based on the trained machine learning model.
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