Systems and methods for 3D point cloud densification
AssigneeHuawei Technologies Co., Ltd.
InventorsEduardo Corral-Soto, Bingbing Liu
Life of this patent
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Abstract
Devices, systems, methods, and media are disclosed for domain adaptation using data densification. Example embodiments described herein receives LiDAR 3D point clouds from a source-domain and introduces interpolated 3D points inferred by a trained deep learning neural network to output a denser version of the input 3D point cloud with increased resolution. The trained domain adaptation network reconstructs the source-domain 3D point cloud data, generates translation vectors to compute interpolated 3D point cloud data and merges the reconstructed 3D point cloud data and the interpolated 3D point cloud data to output a densified 3D point cloud resembling data 3D point clouds captured generated by the target LiDAR sensor from the source-domain.
Claims20
A method comprising:
- receiving a source-domain 3D point cloud comprising a set of source-domain 3D data;
- encoding, using a trained domain adaptation network, the set of source-domain 3D data to generate encoded source-domain 3D data;
- decoding, using the trained domain adaptation network, the encoded source-domain 3D data to generate one or more 3D translation vectors;
- decoding, using the trained domain adaptation network, the encoded source-domain 3D data to generate a reconstructed 3D point cloud comprising a set of reconstructed 3D data;
- computing a set of interpolated 3D data based on the one or more 3D translation vectors and the set of reconstructed 3D data;
- and
- concatenating the set of interpolated 3D data and the set of source-domain 3D data to obtain a densified 3D point cloud comprising a set of densified 3D data.
A system comprising:
- one or more processors;
- one or more memories storing machine-executable instructions, which, when executed by the one or more processors, cause the system to:
- receive a source-domain 3D point cloud comprising a set of source-domain 3D data;
- encode, using a trained domain adaptation network, the set of source-domain 3D data to generate encoded source-domain 3D data;
- decode, using the trained domain adaptation network, the encoded source-domain 3D data to generate one or more 3D translation vectors;
- decode, using the trained domain adaptation network, the encoded source-domain 3D data to generate a reconstructed 3D point cloud comprising a set of reconstructed 3D data;
- compute a set of interpolated 3D data based on the one or more 3D translation vectors and the set of reconstructed 3D data;
- and
- concatenate the set of interpolated 3D data and the set of source-domain 3D data to obtain a densified 3D point cloud comprising a set of densified 3D data.
A non-transitory computer-readable medium having machine-executable instructions stored thereon which, when executed by one or more processors of a computing system, cause the computing system to:
- receive a source-domain 3D point cloud comprising a set of source-domain 3D data;
- encode, using a trained domain adaptation network, the set of source-domain 3D data to generate encoded source-domain 3D data;
- decode, using the trained domain adaptation network, the encoded source-domain 3D data to generate one or more 3D translation vectors;
- decode, using the trained domain adaptation network, the encoded source-domain 3D data to generate a reconstructed 3D point cloud comprising a set of reconstructed 3D data;
- compute a set of interpolated 3D data based on the one or more 3D translation vectors and the set of reconstructed 3D data;
- and
- concatenate the set of interpolated 3D data and the set of source-domain 3D data to obtain a densified 3D point cloud comprising a set of densified 3D data.
Dependencies are inferred from the claim text.