Learning device, identification device, learning method, identification method, learning program, and identification program

An identification device comprising a processor configured to execute operations comprising:

• acquiring a plurality of identification target points by sampling a target point group that is a set of three-dimensional target points;
• calculating relative coordinates of a neighboring point that is a target point set for the identification target point with respect to the identification target point, for each of the plurality of identification target points;
• acquiring class labels of the plurality of identification target points and validity of the class labels of each of the plurality of identification target points with respect to the neighboring points by inputting coordinates of the plurality of identification target points and the relative coordinates of the neighboring points with respect to each of the plurality of identification target points into the class label assigning learned model;
• and
• assigning the class label acquired by the label acquisition unit to the plurality of identification target points, assigns the class labels to the neighboring points for each of the plurality of identification target points when the validity of the class label is included in a range determined by a predetermined threshold value, and identifies the class labels of the identification target points and the neighboring points.

A computer implemented method for learning, comprising:

• acquiring learning data, wherein the learning data includes at least:
• first data including coordinates of a learning identification target point sampled from a learning target points expressed as a set of three-dimensional target points for learning,
• second data including relative coordinates of a plurality of learning neighboring points associated with the learning identification target point relative to to the learning identification target point,
• third data including teacher data of a class label of the learning identification target point, and
• fourth data including teacher data of validity of the class label of the learning identification target point are associated with each other;
• and
• learning, based on the learning data, a class label assigning model, wherein the class label assigning model includes:
• a first model, wherein the first model receives the second data including the relative coordinates of the plurality of neighboring points relative to the learning identification target point and outputs conversion coordinates and a first feature quantity, the conversion coordinates are obtained by converting the relative coordinates of the learning neighboring points,
• a second model, wherein the second model receives the first data including the coordinates of the learning identification target point and the first feature quantity as input and outputs a second feature quantity and the third data including the class label of the learning identification target point, and
• a third model, wherein the third model receives the second feature quantity and the conversion coordinates and outputs validity data indicating validity of respective class labels of the plurality of learning neighboring points, wherein the conversion coordinates are obtained by converting the relative coordinates of the neighboring point.