Abstract: An object of the present invention is to provide an equipment state detecting device, an equipment state detecting method, and a program that can create a 3D model of a cable based on three-dimensional coordinates acquired using a laser scanner or the like, and precisely estimate the tension for the entirety of the cable even if the entirety of the cable is not three-dimensionally modeled in the cable model. An equipment state detecting device of the present invention creates a 3D model of a cable based on three-dimensional coordinates acquired using a laser scanner or the like, acquires a sag and a straight line connecting ends of the 3D model based on the 3D model, and calculates the tension of the cable using a known cable load per unit length.

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 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 object of the present disclosure is to provide a technique for creating a three-dimensional model of a line-like structure from a point cloud obtained using three-dimensional laser measuring equipment and detecting a three-dimensional model of a cable.

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 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: An object of the present disclosure is to provide a technique for creating a three-dimensional model of a line-like structure from a point cloud obtained using three-dimensional laser measuring equipment and detecting a three-dimensional model of a cable.

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.

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

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.

Abstract: An object of the present invention is to provide an equipment state detecting device, an equipment state detecting method, and a program that can create a 3D model of a cable based on three-dimensional coordinates acquired using a laser scanner or the like, and precisely estimate the tension for the entirety of the cable even if the entirety of the cable is not three-dimensionally modeled in the cable model. An equipment state detecting device of the present invention creates a 3D model of a cable based on three-dimensional coordinates acquired using a laser scanner or the like, acquires a sag and a straight line connecting ends of the 3D model based on the 3D model, and calculates the tension of the cable using a known cable load per unit length.

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.