US 12,067,763 B2
Three-dimensional point cloud label learning estimation device, three-dimensional point cloud label learning estimation method, and 3D point cloud label learning estimation program
Yasuhiro Yao, Tokyo (JP); Hitoshi Niigaki, Tokyo (JP); Kana Kurata, Tokyo (JP); Kazuhiko Murasaki, Tokyo (JP); Shingo Ando, Tokyo (JP); and Atsushi Sagata, Tokyo (JP)
Assigned to NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo (JP)
Appl. No. 17/612,373
Filed by NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo (JP)
PCT Filed May 23, 2019, PCT No. PCT/JP2019/020452
§ 371(c)(1), (2) Date Nov. 18, 2021,
PCT Pub. No. WO2020/235079, PCT Pub. Date Nov. 26, 2020.
Prior Publication US 2022/0222933 A1, Jul. 14, 2022
Int. Cl. G06V 10/82 (2022.01); G06N 3/08 (2023.01); G06V 10/40 (2022.01); G06V 10/762 (2022.01)
CPC G06V 10/82 (2022.01) [G06N 3/08 (2013.01); G06V 10/40 (2022.01); G06V 10/762 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A three-dimensional point cloud label learning and estimation device comprising:
a cluster generator configured to cluster a three-dimensional point cloud into clusters;
a learner configured to cause a neural network to learn to estimate a label corresponding to an object to which points contained in each of the clusters belong; and
an estimator configured to estimate a label for the cluster using the neural network learned at the learning unit,
wherein the neural network uses a total sum of sigmoid function values when performing feature extraction on the cluster, wherein
during learning, the cluster generator outputs a labeled clustering result by performing clustering on a three-dimensional point cloud with application of learning point cloud labels and clustering parameters, the learning point cloud labels being labels previously assigned to respective points in the three-dimensional point cloud, and during estimation, the cluster generator performs clustering on a target three-dimensional point cloud with application of the clustering parameters and outputs an unlabeled clustering result,
the learner uses the labeled clustering result and deep neural network hyper-parameters to learn label estimation parameters for estimating labels to be assigned to respective clusters that result from the clustering at the cluster generator, and outputs learned deep neural network parameters, and
the estimator estimates a label for each cluster in the unlabeled clustering result by using the unlabeled clustering result, the deep neural network hyper-parameters, and the learned deep neural network parameters output by the learner.