公开(公告)号：US20210158016A1

公开(公告)日：2021-05-27

申请号：US17048083

申请日：2019-04-16

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Yasuhiro YAO ,  Hitoshi NIIGAKI ,  Ken TSUTSUGUCHI ,  Tetsuya KINEBUCHI

IPC: G06K9/00 ,  G06T7/11 ,  G06K9/62

Abstract: A large-scale point cloud having no limitation on the range or the number of points is set as an object, and labels are attached to the points constituting the object regardless of the type of object.
A three-dimensional point cloud label learning apparatus 10A includes a ground-based height calculation unit that receives a three-dimensional point cloud and a ground surface height as inputs and outputs a three-dimensional point cloud with a ground-based height, an intensity-RGB conversion unit that receives the three-dimensional point cloud with the ground-based height as an input and outputs an intensity-RGB converted three-dimensional point cloud with the ground-based height, a supervoxel clustering unit that receives the intensity-RGB converted three-dimensional point cloud with the ground-based height, a point cloud label for learning, and a clustering hyperparameter as inputs and outputs supervoxel data with a correct answer label, and a deep neural network learning unit that receives the supervoxel data with the correct answer label and a deep neural network hyperparameter as inputs and outputs a learned deep neural network parameter.

公开(公告)号：US20230040195A1

公开(公告)日：2023-02-09

申请号：US17792655

申请日：2020-01-15

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Kana KURATA ,  Yasuhiro YAO ,  Shingo ANDO ,  Jun SHIMAMURA

IPC: G01S13/89 ,  G06K9/00 ,  G06N5/04

Abstract: A class label of a three-dimensional point cloud can be identified with high performance. The key point choice unit 22 extracts a key point cloud 35 including three-dimensional points efficiently representing features of an object and a non-key point cloud 37. A inference unit 24 takes, as representative points, a plurality of points selected by down-sampling from each of the key point cloud 35 and the non-key point cloud 37, extracts, with respect to each of the representative points, a feature of each representative point from coordinates and the feature of the representative point and coordinates and features of neighboring points positioned near the representative point. The inference unit 24 extracts features of a plurality of new representative points from the coordinates and the features of the plurality of representative points, coordinates and features of a plurality of three-dimensional points before sampling which are the new representative points, and coordinates and features of neighboring points positioned near the new representative points. The inference unit 24 derives a class label from the coordinates and features of the plurality of representative points, or the coordinates and features of the plurality of new representative points, and outputs the class label.

公开(公告)号：US20220392193A1

公开(公告)日：2022-12-08

申请号：US17775837

申请日：2019-11-11

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Yasuhiro YAO ,  Hitoshi NIIGAKI ,  Shingo ANDO ,  Jun SHIMAMURA

IPC: G06V10/762 ,  G06V10/77 ,  G06V10/82

Abstract: A clustering unit (101) divides an input three-dimensional point cloud into a plurality of clusters and outputs cluster data, a surrounding point sampling unit (102) extracts, for each of the plurality of clusters, a surrounding three-dimensional point cloud present within a predetermined distance of the cluster based on the three-dimensional point cloud and the cluster data, a learning unit (103) receives, as inputs, extended cluster data including information on a three-dimensional point cloud included in each cluster obtained by the division and information on the extracted surrounding three-dimensional point cloud and a correct answer label indicative of an object to which the three-dimensional point cloud included in each cluster belongs, and learns a parameter of a DNN for estimating a label of each cluster from the extended cluster data, and an estimation unit (104) inputs the extended cluster data related to the cluster of which the label is unknown to the DNN of which the parameter is trained to estimate the label of each cluster.

公开(公告)号：US20250045875A1

公开(公告)日：2025-02-06

申请号：US18716828

申请日：2021-12-07

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Shogo SATO ,  Yasuhiro YAO ,  Shingo ANDO ,  Jun SHIMAMURA

IPC: G06T5/50 ,  G06T5/70 ,  G06T7/13 ,  G06T7/90 ,  G06V10/44 ,  G06V10/60 ,  G06V10/762 ,  G06V20/70

Abstract: An input processing unit receives an image and a three-dimensional point cloud including three-dimensional points having reflection intensity on a surface of an object for which at least a relationship between an image capturing position and a measurement position is obtained in advance, and obtains pixel positions on the image corresponding to the respective three-dimensional points of the three-dimensional point cloud. A shadow region estimation unit performs clustering on pixels of the image on the basis of pixel values and pixel positions, obtains an average reflection intensity and an average value of quantified color information for each of clusters, and estimates a shadow region by performing comparison in the average reflection intensity and the average value of the color information between the clusters. A shadow correction unit corrects pixel values of the shadow region from the shadow region estimated and the image.

公开(公告)号：US20240281501A1

公开(公告)日：2024-08-22

申请号：US18292226

申请日：2021-07-29

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Kana KURATA ,  Yasuhiro YAO ,  Naoki ITO ,  Jun SHIMAMURA

IPC: G06F18/241

CPC classification number: G06F18/241

Abstract: A three-dimensional point group is accurately identified on the basis of GIS data in which a geographical position of an object is defined by a polygon, a line, or a point.
An initial label assignment unit (101) assigns a label indicating an object or a type of the object corresponding to some three-dimensional points included in a three-dimensional point group to the three-dimensional points on the basis of GIS data indicating a geographical position of the object by a polygon formed by connecting a plurality of coordinate points, a line formed by connecting a plurality of coordinate points, or one coordinate point itself, and an estimation unit (112) estimates a label of three-dimensional points to which the label has not been assigned by propagating the label of the three-dimensional points to which the label has been assigned to the three-dimensional points to which the label has not been assigned based on similarity between the three-dimensional points to which the label has been assigned and the three-dimensional points to which the label has not been assigned.

公开(公告)号：US20220254173A1

公开(公告)日：2022-08-11

申请号：US17627883

申请日：2019-07-23

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Yasuhiro YAO ,  Hitoshi NIIGAKI ,  Kana KURATA ,  Shingo ANDO ,  Atsushi SAGATA

IPC: G06V20/64 ,  G06T7/60 ,  G06V10/46 ,  G06V10/762 ,  G06V10/764

Abstract: A mesh structure facility detection device that detects data corresponding to a mesh structure facility from three-dimensional structure data representing a space including an outer shape of an object, includes: a projection unit that projects the three-dimensional structure data in a predetermined direction to obtain two-dimensional structure data; and a detection unit that detects a point included in a region in which the two-dimensional structure data has a density of more than or equal to a predetermined threshold value as a point corresponding to the mesh structure facility.

公开(公告)号：US20250052590A1

公开(公告)日：2025-02-13

申请号：US18717379

申请日：2021-12-08

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Taiga YOSHIDA ,  Yasuhiro YAO ,  Naoki ITO ,  Jun SHIMAMURA

IPC: G01C21/00

Abstract: A road boundary detection device is a road boundary detection device that acquires a set of lines corresponding to a road boundary from point cloud data as road boundary information. The road boundary detection device includes: a candidate point detection unit that detects each point of road boundary candidates corresponding to candidates of a road boundary from the point cloud data; a candidate point clustering unit that clusters each point of the road boundary candidates; an adjacent cluster reduction unit that reduces a cluster from a distribution of points in clusters in an adjacency relationship by using a predetermined cluster reduction method; a line fitting unit that fits one or more straight lines or curved lines to one or more of the clusters and output fitted lines as road boundary candidates; a line connecting unit that connects some of the fitted lines by using a predetermined analysis method; and an information output unit that outputs a calculated line as the road boundary information.

公开(公告)号：US20230409964A1

公开(公告)日：2023-12-21

申请号：US18035090

申请日：2020-11-05

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Kana KURATA ,  Yasuhiro YAO ,  Naoki ITO ,  Shingo ANDO ,  Jun SHIMAMURA

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: An identification device acquires a plurality of identification target points by sampling a target point group that is a set of three-dimensional target points. The identification device calculates relative coordinates of a neighboring point of the identification target point with respect to the identification target point. The identification device inputs coordinates of the plurality of identification target points and relative coordinates of neighboring points with respect to each of the plurality of identification target points into a class label assigning learned model to acquire class labels of the plurality of identification target points and validity of the class labels with respect to the neighboring points for each of the plurality of identification target points. The identification device assigns the class labels 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 point and the neighboring point.

公开(公告)号：US20220230347A1

公开(公告)日：2022-07-21

申请号：US17608975

申请日：2019-05-08

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Hitoshi NIIGAKI ,  Masaki WAKI ,  Masaaki INOUE ,  Yasuhiro YAO ,  Tomoya SHIMIZU ,  Hiroyuki OSHIDA ,  Kana KURATA ,  Shingo ANDO ,  Atsushi SAGATA

IPC: G06T7/66 ,  G06V10/25 ,  G06V10/75

Abstract: It is possible to estimate a slack level accurately in consideration of a shape of a deformed cable. A point cloud analysis device sets a plurality of regions of interest obtained by window-searching a wire model including a quadratic curve model representing a cable obtained from a point cloud consisting of three-dimensional points on an object, the region of interest being divided into a first region and a second region. The point cloud analysis device compares information on the first region with information on the second region based on the point cloud included in the region of interest and the quadratic curve model for each of the plurality of regions of interest, calculates a degree of division boundary representing a degree to which a division position between the first region and the second region of the plurality of regions of interest is a branch point of the cable, and detects a division boundary point that is a branch point of a cable represented by the quadratic curve model based on the degree of division boundary calculated for each of the plurality of regions of interest.

公开(公告)号：US20250037402A1

公开(公告)日：2025-01-30

申请号：US18716509

申请日：2021-12-06

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Yasuhiro YAO ,  Kana KURATA ,  Shingo ANDO ,  Jun SHIMAMURA

IPC: G06T19/20

Abstract: A position and posture estimation device acquires three-dimensional point cloud data at each of times and position data at each of times, the three-dimensional point cloud data being measured every time a first time elapses, the position data being measured every time a second time longer than the first time elapses. The position and posture estimation device estimates a local position in a local coordinate system and a local posture in the local coordinate system. The position and posture estimation device estimates an estimated absolute position and an estimated absolute posture in an absolute coordinate system every time the position data is acquired. The position and posture estimation device generates provisional three-dimensional point cloud data in the absolute coordinate system every time the position data is acquired. The position and posture estimation device generates composite data obtained by integrating the provisional three-dimensional point cloud data and map point cloud data generated from three-dimensional point cloud data previously measured, and corrects the estimated absolute position and the estimated absolute posture to increase a degree of coincidence between the composite data and the map point cloud data.