Preserving geometry details in a sequence of tracked meshes

A method, comprising:

• acquiring a set of three-dimensional (3D) scans of an object of interest;
• acquiring a sequence of tracked meshes of the object of interest,
• wherein the acquired sequence of tracked meshes comprises a set of tracked meshes which temporally corresponds to the acquired set of 3D scans;
• generating a set of displacement maps based on differences between the set of tracked meshes and the acquired set of 3D scans;
• computing a plurality of vectors, each of which includes surface tension values associated with mesh vertices of a corresponding tracked mesh of the sequence of tracked meshes;
• training a neural network model on a task of displacement map generation, based on the computed set of displacement maps and a corresponding set of vectors of the computed plurality of vectors;
• applying the trained neural network model on the computed plurality of vectors to generate a plurality of displacement maps;
• and
• updating each tracked mesh of the acquired sequence of tracked meshes based on a corresponding displacement map of the generated plurality of displacement maps.

A non-transitory computer-readable medium having stored thereon computer implemented instructions that, when executed by an electronic device, causes the electronic device to execute operations, the operations comprising:

• acquiring a set of three-dimensional (3D) scans of an object of interest;
• acquiring a sequence of tracked meshes of the object of interest,
• wherein the acquired sequence of tracked meshes comprises a set of tracked meshes which temporally corresponds to the acquired set of 3D scans;
• generating a set of displacement maps based on differences between the set of tracked meshes and the acquired set of 3D scans;
• computing a plurality of vectors, each of which includes surface tension values associated with mesh vertices of a corresponding tracked mesh of the sequence of tracked meshes;
• training a neural network model on a task of displacement map generation, based on the computed set of displacement maps and a corresponding set of vectors of the computed plurality of vectors;
• applying the trained neural network model on the computed plurality of vectors to generate a plurality of displacement maps;
• and
• updating each tracked mesh of the acquired sequence of tracked meshes based on a corresponding displacement map of the generated plurality of displacement maps.