Nippon Telegraph and Telephone Corporation

Yasuhiro Yao

Atsushi Sagata

Shingo Ando

Hitoshi Niigaki

Kana Kurata

Hiroyuki Oshida

Tomoya Shimizu

Masaaki Inoue

Kazuhiko Murasaki

Yukihiro Goto

Tetsuya Kinebuchi

Ryuji Honda

Ken Tsutsuguchi

Shigehiro Matsuda

Masaki Waki

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. In a label estimation apparatus for estimating a label to be assigned to a point that has not been labeled using a label of a point that has been labeled among points included in a point group, a confidence derivation unittakes a point that has not been labeled within a point group including a point that has been labeled and the point that has not been labeled as a target point and estimates a class of the target point and a likelihood indicating a confidence of an estimation result of the class from a set of points included in the point group, a priority derivation unitobtains a distance between the target point and a point that has been assigned the same label as a label corresponding to the estimated class as a priority used to determine whether the estimated class is appropriate, and a label determination unitdetermines whether the estimated class is appropriate using at least an index based on the distance.

Label estimation device, label estimation method, and label estimation program

Annotation can be easily performed on a three-dimensional point cloud and a working time can be reduced. An interface unitdisplays 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 unitcalculates 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 unitcauses the interface unitto 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. An annotation execution unitexecutes annotation indicating the annotation target object for each three-dimensional point included in the selected point cloud cluster.

Point cloud annotation device, method, and program

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 () 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 () estimates a central axis direction based on the point cloud cluster. A direction-dependent local effective length estimation unit () 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.

Point cloud analysis device, method, and program

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.

Three-dimensional point cloud label learning estimation device, three-dimensional point cloud label learning estimation method, and 3D point cloud label learning estimation program

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.

Point cloud analysis device, estimation device, point cloud analysis method, and program

Dense depth information can be generated using only a monocular image and sparse depth information. A depth hyper-resolving apparatusincludes: an input data processing unitthat outputs a hierarchical input image and hierarchical input depth information by resolution conversion in accordance with a predetermined number of tiers for an input image and input depth information; a depth continuity estimation unitthat derives hierarchical estimated depth continuity based on the hierarchical input image; a depth continuity mask deriving unitthat outputs a hierarchical depth continuity mask representing values of locations depending on whether a depth is continuous based on the hierarchical input image and the hierarchical estimated depth continuity; and a cost function minimization unitthat derives hyper-resolved depth information to minimize a cost function expressed by using the hierarchical input depth information, the hierarchical depth continuity mask, and the hyper-resolved depth information.

Depth superresolution device, depth superresolution method, and program

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.

Mesh structure equipment detection apparatus, mesh structure equipment detection method and program

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 apparatusA 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.

Three-dimensional point cloud label learning device, three-dimensional point cloud label estimating device, three-dimensional point cloud label learning method, three-dimensional point cloud label estimating method, and program

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Tokyo, Japan

Yasuhiro Yao