15 results about "Representative point" patented technology

The invention relates to a project supervision quality verification method based on BIM and on-site monitoring data fusion. The method comprises the following steps: constructing a BIM model of a target project; carrying out representative point random sampling on the BIM model, and calculating a quality verification index of a representative point; constructing a representative point set based on all the representative points, wherein the representative point set comprises the representative points and related data thereof; obtaining a target quality verification point outside the representative point set, and obtaining a second three-dimensional coordinate and a quality verification index of the target quality verification point based on the BIM model; and engineering supervision quality verification is carried out on the target quality verification point based on the representative point set and the second three-dimensional coordinates through a preset quality verification model, whether a quality abnormity risk exists is judged, and a result is output. According to the method, the abnormal threshold value can be dynamically adjusted through the spatial relevance between the target quality verification point and the sampling point of the target project, the target quality verification point with the quality abnormal risk can be sensitively captured, and the quality verification result is more accurate.

Aligning a source and target model includes calculating a shape descriptor for a plurality of edge points, grouping the edge points by shape descriptor and selecting representative points for each group. Target and source point pair features (PPFs) are calculated between pairs of representative points on the target and source models, where PPFs defines the relative position and orientation of point pairs. Target PPFs are matched with each source PPF and the point pairs associated with the matched PPFs are transformed to align the location and edge direction of a target PPF point with the location and edge direction of a source PPF point. An angle is determined to align the second target PPF point with the second source PPF point, and a modal angle is found among the determined rotation angles. An output transformation is calculated using the transformations associated with the modal angle.

A point cloud with attributes is obtained. Its geometry is encoded and decoded. Holes are detected in the decoded geometry and metadata representative of coordinates of the holes are generated. In an embodiment, points are added at these locations to obtain a reconstructed geometry. The attributes of the point cloud are transferred on the decoded or on the reconstructed geometry and, the decoded geometry, the transferred attributes, and the metadata are encoded in a data stream. At the decoding side, a data stream representative of a point cloud is obtained. The geometry is decoded. In an embodiment, metadata are used to add point to the decoded geometry. The attributes of the point cloud are decoded the decoded or reconstructed geometry. In the first case, metadata are used to add points to the point cloud and their attributes are estimated according to the decoded attributes.

The invention provides an automatic endoscope following method and device, electronic equipment and a storage medium, and relates to the technical field of automatic control. The method comprises the following steps: identifying a plurality of instruments in an endoscope image; based on the endoscope image, obtaining position information of the instrument in the endoscope image; based on the position information of the multiple instruments, comprehensive representative points of the multiple instruments are obtained; and if the comprehensive representative point deviates from the center of the visual field of the endoscope, performing visual angle following adjustment on the endoscope. The method is used for dynamically balancing the influence of multiple instruments on the view, ensuring that key instruments are always in the center of the view, and solving the view management problem in a multi-instrument scene in the prior art.

In various examples, an environment surrounding an ego-object is visualized using an adaptive 3D bowl that models the environment with a shape that changes based on distance (and direction) to one or more representative point(s) on detected objects. Distance (and direction) to detected objects may be determined using 3D object detection or a top-down 2D or 3D occupancy grid, and used to adapt the shape of the adaptive 3D bowl in various ways (e.g., by sizing its ground plane to fit within the distance to the closest detected object, fitting a shape using an optimization algorithm). The adaptive 3D bowl may be enabled or disabled during each time slice (e.g., based on ego-speed), and the 3D bowl for each time slice may be used to render a visualization of the environment (e.g., a top-down projection image, a textured 3D bowl, and / or a rendered view thereof).

A method for predicting a future position of an object includes capturing a video stream, the video stream including two-dimensional (2D) image data; detecting an object in the two-dimensional (2D) image data; obtaining a posture of the object comprising a plurality of key points; generating a posture history of the object; determining movement characteristics of two or more key points of the object; predicted position data for each of the two or more keypoints is obtained by estimating at least an estimated state of each of the two or more keypoints. Based on the movement characteristics and the attitude history; and predicting a future position of at least a predefined representative point of the object from the predicted position data, where the future position is predicted for two-dimensional (2D) image data of the video stream.

A point cloud with attributes is obtained. Its geometry is encoded and decoded. Holes are detected in the decoded geometry and points are added at these locations to obtain a reconstructed geometry. The attributes of the point cloud are transferred on this reconstructed geometry and, the decoded geometry and the transferred attributes are encoded in a data stream. At the decoding side, a data stream representative of a point cloud is obtained. The geometry is decoded, and holes are detected to obtain a reconstructed geometry. The attributes of the point cloud are transferred on this reconstructed geometry to obtain a point cloud with attributes.

A non-transitory computer-readable storage medium storing an estimation program that causes a computer to execute a process includes specifying representative points of each of training clusters that corresponds to each of labels targeted for estimation; setting boundaries between each of input clusters under a condition that a number of the input clusters and a number of the representative points coincide with each other, the input clusters being generated by clustering in a feature space for input data; acquiring estimation results for the labels with respect to the input data based on a correspondence relationship between the input clusters and the training clusters based on the boundaries; and estimating determination accuracy for the labels by using the machine learning model with respect to the input data based on the estimation results.

Provided are an information processing system, an information processing method, and a program that achieve update of a classification algorithm capable of achieving improvement in accuracy. The information processing system comprises a processor and a memory in which an instruction to be executed by the processor is stored. The processor executes the instruction to: select, from among a plurality of feature points respectively representing the feature amounts of a plurality of images, at least one first representative point for each label attached to the images on the basis of the feature amounts by using a classification algorithm; correct the label of the first representative point on the basis of a determination result of whether the label of the first representative point is correct; and update the classification algorithm on the basis of a classification rule corrected according to the correction of the label of the first representative point.

This invention relates to the field of machine vision technology, specifically to a method and system for positioning the rim of paper cups in a stacked paper cup production line. The invention acquires consecutive frame images of each paper cup; selects representative points of the cup rim contour in each frame; determines the directional deviation angle of each representative point based on its displacement vector in adjacent frames; obtains the sliding influence coefficient of the cup rim position in each frame based on the displacement vector and directional deviation angle; analyzes the fluctuation amplitude of the cup rim position sliding in all frames to determine the degree of sliding influence of each rim; combines this with the hysteresis time to determine the degree of hysteresis deviation of the cup rim position in each frame; and determines the degree of positional mismatch of each rim based on the degree of sliding influence and the degree of hysteresis deviation, thereby determining the correction degree for each rim and correcting its position. This invention can improve the positioning accuracy of paper cup rims and ensure precise alignment in subsequent processes.

The application discloses a kind of map data processing method, device, medium, equipment and cloud platform, belong to map making technical field.The method comprises the following steps: obtaining at least one object surface in map intermediate data and at least one object surface corresponding adjacent surface, at least one object surface and at least one object surface corresponding adjacent surface are simulated combination and obtain simulated combination surface;Simulated combination surface with multiple closed loops is identified as the multi-face with cavity, the closed loop surrounded area inside the multi-face is identified as cavity, and the gap of at least one object surface is determined according to the cavity;The coordinates of one internal point of the gap are extracted as the representative point coordinates of the gap, and the representative point coordinates are de-duplicated to obtain the gap position.The application avoids the problems of large workload, low accuracy, easy redundancy identification and easy omission when identifying the gap in the prior art, improves the efficiency and accuracy of gap identification.

It accurately and reliably recognizes the position and orientation of objects. [Solution] The recognition devices 201 and 202 include: a feature region detection unit 21 that detects at least three feature regions 62 of an object from imaging data 67 obtained by imaging the object 61; a representative point extraction unit 23 that calculates extracted representative points 69 from the imaging data and feature regions and extracts at least three extracted representative points and extracted label information 64 for identifying each extracted representative point; and a position and orientation estimation unit 24 that estimates the position and orientation of an object by aligning the extracted representative points with the reference representative points and the extracted representative points, where the extracted label information corresponding to the extracted representative points and the reference label information 64 for identifying at least three reference representative points having a predefined correct positional relationship coincide.

The alignment system (1) has: an image processing unit (400) that, based on a captured image of a workpiece (10), calculates a center of vibration of a representative point of the workpiece (10), and determines whether the calculated center is within a range of a predetermined precision reference for the representative point; and a motion control unit (200) that controls a driving device (111, 112, 113) that moves the workpiece (10). The motion control unit (200) controls the driving device (111, 112, 113) based on a result of the determination by the image processing unit (400).

A point cloud segmentation method based on Gaussian semantic sampling and multi-scale feature fusion is proposed, comprising the following steps: 1) preprocessing the dataset; 2) designing an adaptive Gaussian semantic sampling algorithm to perform AGSS sampling on the input projection features to obtain a more representative point set; 3) proposing a multi-scale fusion VLAD module to process the features extracted by each layer of the encoder in parallel, constructing a global descriptor with rich details and strong semantic discriminativeness; 4) loading the trained network parameters, performing end-to-end inference on the test set, and outputting the point cloud segmentation results. This invention, through the adaptive Gaussian semantic sampling algorithm and the multi-scale fusion VLAD module, fully mines local spatial topological information, enhances the model's ability to represent local features of complex-shaped targets, and effectively reduces computational overhead through lightweight design. This solves the core problems of current methods, such as insufficient extraction of local geometric information, inaccurate mining of key feature channels and spatial information, and high redundant computational overhead.