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.

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.

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.

Abstract: A point group including a small number of points that have been assigned labels is taken as an input to assign labels to points that have not been assigned labels.

Abstract: Annotation can be easily performed on a three-dimensional point cloud and a working time can be reduced. An interface unit 22 displays a point cloud indicating a three-dimensional point on an object, and receives designation of a three-dimensional point indicating an annotation target object and designation of a three-dimensional point not indicating the annotation target object. A candidate cluster calculation unit 32 calculates a value of a predetermined evaluation function indicating a likelihood of a point cloud cluster being the annotation target object based on the designation of a three-dimensional point for point cloud clusters obtained by clustering the point clouds. A cluster selection and storage designation unit 34 causes the interface unit 22 to display the point cloud clusters in descending order of the value of the evaluation function, and receives a selection of a point cloud cluster to be annotated.

Abstract: Provided is a point cloud analysis device that curbs a decrease in model estimation accuracy due to a laser measurement point cloud. A clustering unit (30) clusters a point cloud representing a three-dimensional point on an object obtained by a measurement unit mounted on a moving body and performing measurement while scanning a measurement position, within a scan line, to obtain a point cloud cluster. A central axis direction estimation unit (32) estimates a central axis direction based on the point cloud cluster. A direction-dependent local effective length estimation unit (34) estimates a local effective length based on an estimated central axis direction and an interval of scan lines, the local effective length being a length when a length of projection of the point cloud cluster in a central axis direction for each of the point cloud clusters is interpolated by an amount of a loss part of the point cloud.

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.

Abstract: A three-dimensional point cloud label learning and estimation device includes: a clustering unit that clusters a three-dimensional point cloud into clusters; a learning unit that makes a neural network learn to estimate a label corresponding to an object to which points contained in each of the clusters belong; and an estimation unit that estimates a label for the cluster using the neural network learned at the learning unit. In the three-dimensional point cloud label learning and estimation device, the neural network uses a total sum of sigmoid function values (sum of sigmoid) when performing feature extraction on the cluster.

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.

Abstract: Dense depth information can be generated using only a monocular image and sparse depth information.

Abstract: A mesh structure facility detection device detects data corresponding to a mesh structure facility from three-dimensional structure data representing a space including an outer shape of an object, and projects the three-dimensional structure data in a predetermined direction to obtain two-dimensional structure data; and 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.

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.

Abstract: Annotation can be easily performed on a three-dimensional point cloud and a working time can be reduced. An interface unit 22 displays a point cloud indicating a three-dimensional point on an object, and receives designation of a three-dimensional point indicating an annotation target object and designation of a three-dimensional point not indicating the annotation target object. A candidate cluster calculation unit 32 calculates a value of a predetermined evaluation function indicating a likelihood of a point cloud cluster being the annotation target object based on the designation of a three-dimensional point for point cloud clusters obtained by clustering the point clouds. A cluster selection and storage designation unit 34 causes the interface unit 22 to display the point cloud clusters in descending order of the value of the evaluation function, and receives a selection of a point cloud cluster to be annotated.

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.

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 esti

Abstract: A point group including a small number of points that have been assigned labels is taken as an input to assign labels to points that have not been assigned labels.

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.

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.

Abstract: A three-dimensional point cloud label learning and estimation device includes: a clustering unit that clusters a three-dimensional point cloud into clusters; a learning unit that makes a neural network learn to estimate a label corresponding to an object to which points contained in each of the clusters belong; and an estimation unit that estimates a label for the cluster using the neural network learned at the learning unit. In the three-dimensional point cloud label learning and estimation device, the neural network uses a total sum of sigmoid function values (sum of sigmoid) when performing feature extraction on the cluster.

Abstract: Provided is a point cloud analysis device that curbs a decrease in model estimation accuracy due to a laser measurement point cloud. A clustering unit (30) clusters a point cloud representing a three-dimensional point on an object obtained by a measurement unit mounted on a moving body and performing measurement while scanning a measurement position, within a scan line, to obtain a point cloud cluster. A central axis direction estimation unit (32) estimates a central axis direction based on the point cloud cluster. A direction-dependent local effective length estimation unit (34) estimates a local effective length based on an estimated central axis direction and an interval of scan lines, the local effective length being a length when a length of projection of the point cloud cluster in a central axis direction for each of the point cloud clusters is interpolated by an amount of a loss part of the point cloud.