US 12,462,438 B2
Machine-learning for 3D object detection
Asma Rejeb Sfar, Vélizy-Villacoublay (FR); Tom Durand, Vélizy-Villacoublay (FR); and Ashad Hosenbocus, Vélizy-Villacoublay (FR)
Assigned to Dassault Systemes, Velizy-Villacoublay (FR)
Filed by Dassault Systemes, Vélizy-Villacoublay (FR)
Filed on Dec. 16, 2021, as Appl. No. 17/553,403.
Claims priority of application No. 20306588 (EP), filed on Dec. 16, 2020.
Prior Publication US 2022/0189070 A1, Jun. 16, 2022
Int. Cl. G06T 9/00 (2006.01); G06N 3/088 (2023.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01)
CPC G06T 9/002 (2013.01) [G06N 3/088 (2013.01); G06V 10/761 (2022.01); G06V 10/82 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method of applying a neural network learnt according to machine-learning for learning a neural network configured to encode a super-point of a 3D point cloud into a latent vector, the method of machine-learning including obtaining a dataset of super-points, each super-point being a set of points of a 3D point cloud, the set of points representing at least a part of an object, and learning the neural network based on the dataset of super-points, the learning comprising minimizing a loss penalizing a disparity between two super-points,
the method comprising:
obtaining one or more first super-points of a first 3D point cloud representing a 3D scene; and
obtaining one or more second super-points of a second 3D point cloud representing a 3D object;
encoding, by applying the neural network, the one or more first super-points each into a respective first latent vector and the one or more second super-points each into a respective second latent vector; and
determining a similarity between each first super-point of one or more first super-points and each second super-point of one or more second super-points, by, for said each first super-point and said each second super-point, computing a similarity measure between the respective first latent vector encoding the first super-point and the respective second latent vector encoding the second super-point,
wherein the obtaining the one or more first super-points further includes:
obtaining one or more initial super-points of the first 3D point cloud; and
filtering the one or more initial super-points by selecting, among the initial super-points, each initial super-point for which:
a disparity between dimensions of the respective initial super-point and dimensions of at least one second super-point of the one or more second super-points is smaller than a predefined threshold; and
a disparity between a position of the respective initial super-point and a position of at least one second super-point of the one or more second super-points is smaller than a predefined threshold,
the selected initial super-points being the one or more first super-points, and
wherein the filtering further includes selecting, among the initial super-points, each initial super-point for which:
a distance between each dimension of the super-point and a corresponding dimension of at least one second super-point of the one or more second super-points is smaller than a predefined threshold;
a ratio between each dimension of the super-point and a corresponding dimension of at least one second super-point of the one or more second super-points is smaller than a maximal ratio and larger than a minimal ratio; and
a difference between a relative height from a closest support surface of the super-point and a relative height from a closest support surface of at least one second super-point of the one or more second super-points is smaller than a predefined threshold.