Self-supervised 3d point cloud abstraction
AssigneeInterDigital Patent Holdings, Inc.
InventorsRuoyu Wang, Muhammad Asad Lodhi, Jiahao Pang, Dong Tian
Life of this patent
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Abstract
A method for adaptively abstracting a point cloud includes initializing a set of primitives associated with a query shape and a set of query parameters. For each primitive a local point set is accessed using the set of query parameters and the query shape associated with the primitive. For each local point set, using a first neural network, a descriptor vector comprising a sub-vector for a primitive update and a sub-vector for a local descriptor is determined. The set of primitives is updated based on the descriptor vector for each local point set.
Claims15
A method for adaptively abstracting a point cloud, the method comprising:
- initializing a set of primitives associated with a query shape and a set of query parameters;
- for each primitive, accessing a local point set using the set of query parameters and the query shape associated with the primitive;
- for each local point set, determining, using a first neural network, a descriptor vector comprising a sub-vector for a primitive update and a sub-vector for a local descriptor;
- and
- updating the set of primitives based on the descriptor vector for each local point set.
A device comprising a processor associated with a memory, wherein the processor is configured to:
- initialize a set of primitives associated with a query shape and a set of query parameters;
- for each primitive, access a local point set using the set of query parameters and the query shape associated with the primitive;
- for each local point set, determine, using a first neural network, a descriptor vector comprising a sub-vector for a primitive update and a sub-vector for a local descriptor;
- and
- update the set of primitives based on the descriptor vector for each local point set.
A method for reconstructing a point cloud from a set of primitives comprising a local descriptor and a global descriptor, the method comprising:
- determining a sampling distribution in a space of the point cloud based on the primitives;
- determining distribution parameters, based on the local descriptor, using a first neural network;
- generating points of the primitives from the distribution parameters;
- and
- shifting and gluing the set of primitives and the generated points, based on the global descriptor, using a second neural network.
A device comprising a processor associated with a memory, the processor being configured to, for a set of primitives comprising a local descriptor and a global descriptor:
- determine a sampling distribution in a space of the point cloud based on the primitives;
- determine distribution parameters, based on the local descriptor, using a first neural network;
- generate points of the primitives from the distribution parameters;
- and
- shift and glue the set of primitives and the sampled points, based on the global descriptor, using a second neural network.
(canceled)
Dependencies are inferred from the claim text.