20 results about "Cost aggregation" patented technology

This invention belongs to the field of production cost management and cost accounting, specifically involving a method, equipment, and medium for dynamic reconstruction of hierarchical codes in a cost calculation BOM. The method constructs a cost calculation BOM, assigns initial hierarchical codes, and detects cyclic nodes. Based on cyclic node information and calculation requirements, an adjustment strategy is determined, and a unified hierarchical reconstruction based on material codes or cost centers is performed. Hierarchical aggregation and alignment are performed on associated materials of joint by-products and object groups. The method achieves dynamic optimization of the BOM hierarchical codes through cyclic node awareness strategy selection, unified hierarchical reconstruction, and collaborative adjustment of special nodes. Reliability of cost convolution calculation is ensured through cyclic node detection and strategy binding. Hierarchical unification based on material codes or cost centers eliminates hierarchical redundancy for identical materials, improving the accuracy of cost aggregation. Collaborative adjustment of the hierarchical levels of joint by-products and associated materials of object groups ensures consistency between the cost convolution path and process configuration.

The invention relates to a 2d binocular depth calculation method capable of running in real time at an embedded terminal. The method comprises the following steps: respectively extracting feature maps of a left eye image and a right eye image; constructing a 3D cost body of channel dimension connection based on the feature map; cost body aggregation, wherein the cost body aggregation comprises the step of performing cost aggregation on the 3D cost body by using 2D grouping convolution of which the grouping number is a parallax candidate number D; and calculating a disparity map based on the aggregated cost body. The embodiment of the invention can be operated in real time at the embedded end, and the output value is within the range allowed by the precision.

The application discloses an infrared stereo matching method based on cost group attention and edge reinforcement, relates to the binocular vision technology field in computer vision, and solves the technical problem of low infrared stereo matching precision in a low-recognition environment.The technical solution points are as follows:multi-scale edge features of an original image are extracted based on a Canny edge detection algorithm and an edge feature extraction network;an edge reinforcement module is constructed in combination with the multi-scale edge features and original image features to obtain fusion features;the fusion features are used to construct a cost group attention module;and a predicted disparity map is obtained through cost aggregation and disparity regression.The edge reinforcement module and the cost group attention module are innovatively adopted, which can respectively supplement and focus on beneficial features in the infrared stereo matching process, so that better stereo matching precision is obtained.

The invention provides a cost calculation restoration method and device supporting a self-defined restoration range, equipment and a medium, belongs to the technical field of cost accounting, configures the cost restoration range, creates an intermediate table for cost restoration calculation, and defines input and output functions. Wherein the cost center selection range of the meter body is constrained by the accounting organization and the range type of the meter head. And receiving a to-be-restored cost calculation single header ID and a restoration range header ID, and initializing the maximum level to be 1. And updating the material unit price and the completion cost of the reset unit price record according to the hierarchy from high to low, summarizing the update header data and transmitting the latest cost price. And calculating a quantity / amount coefficient, and adjusting and inserting the associated table body data. And dumping the intermediate table data to a cost reduction calculation sheet, and filling missing source accounting organization / cost center information. According to the method, cross-organization automatic reduction of cost data is realized, the accuracy and transparency of cost calculation are improved, and multi-organization cost management and decision are effectively supported.

The application discloses a binocular stereo matching FPGA accelerator architecture, method and device, belongs to the binocular stereo vision field, and comprises a line buffer module, an initial cost calculation module, a Sobel edge detection module, a cost aggregation module, a right view cost matrix construction module, a uniqueness detection module, a left-right consistency detection module, a hole filling module and a sub-pixel interpolation module. The application solves the problems of low precision, poor efficiency and low resource utilization rate of binocular stereo matching, improves the precision, and guarantees the real-time performance and the efficiency of operation resources.

The invention relates to the technical field of cloud edge collaboration, solves the technical problems that in the prior art, conflict detection and solution means are single, complex and changeable scenes are difficult to dynamically adapt, and real-time conflicts cannot be fully considered, and particularly relates to an agricultural equipment information scheduling management method and system applying cloud edge collaboration. The method comprises the steps that original environment data and equipment data of agricultural equipment information are collected, expected data are preset, and a standardized environment data set, a standardized equipment data set and a standardized expected data set are generated through classification preprocessing. According to the method, the equipment capability is accurately measured through comprehensive efficiency sorting, the time, energy consumption, efficiency and load can be comprehensively considered through multi-target cost aggregation, the Hungary algorithm is improved to ensure global optimal matching, the reasonability of task allocation, the resource utilization rate and the system robustness are remarkably improved, and the processing efficiency of sudden changes of the environment is greatly improved.

The present application relates to the technical field of three-dimensional reconstruction, and in particular to an object three-dimensional reconstruction system based on deep learning, which introduces an adaptive cost aggregation method with visibility perception for cost volume aggregation, acquires the visibility of pixel points in the view through a network, and can improve the reconstruction integrity of the occluded area; a variance-based method is used to predict the disparity range of each pixel, and a spatially-varying depth hypothesis surface is constructed for depth estimation in the next stage, and a residual and channel attention guided fusion depth map optimization module is proposed in the last stage to obtain an optimized depth map; an improved depth map fusion algorithm is used to combine the pixel point and 3D point re-projection error for consistency checking to obtain a dense point cloud. Quantitative and qualitative comparison results of the present application and other methods on the DTU dataset show that the present application can reconstruct a scene with better details, and achieves the purposes of reducing GPU memory consumption and computation time.

The present application relates to image stereo reconstruction method, specifically to multi-view stereo reconstruction method based on frequency perception feature enhancement and cost aggregation. In order to solve the problem that the existing learning-based multi-view stereo reconstruction method lacks frequency perception ability and has limited processing ability for weak texture, noise, illumination change and color distortion, the present application realizes multi-view stereo reconstruction through the steps of acquiring multi-view images, calculating multi-scale frequency perception features, calculating initial cost volume, embedding frequency information into the initial cost volume, calculating depth estimation and back projection in turn. When calculating multi-scale frequency perception features, a double-branch frequency component enhancement module is set to process the lossless approximate low-frequency component and high-frequency component of the input image decomposed by wavelet transform layer, so as to improve the global consistency and local detail expression of depth estimation.

This invention provides an automatic cost transfer device, an automatic cost transfer method, and an automatic cost transfer program that link the final package product and its constituent products in a hierarchical structure, and perform cost aggregation processing and transfers for each hierarchical level. [Solution] The present invention provides an automatic cost transfer device that has access to production number management data, production number level data, production number arrangement data, purchase data, and inventory data, and includes a cost transfer calculation unit that references the purchase data and the inventory data, automatically calculates the cost starting from products with lower production number level data, transfers the calculated cost to products with higher production numbers linked in the production number management data, and calculates the cost of the package product.

A method and a system for extracting a dense disparity map based on multi-sensor fusion are provided. The method includes: obtaining a left-eye image and a right-eye image in a same road scenario, and point cloud information about the road scenario; generating an initial cost volume map set in accordance with the left-eye image, the right-eye image and the point cloud information; performing multidirectional cost aggregation in accordance with the point cloud information and the initial cost volume map set, and creating an energy function in accordance with the cost aggregation; and solving an optimum disparity for each pixel in the left-eye image in accordance with the energy function, so as to generate the dense disparity map.

This invention proposes an optical flow estimation method based on hierarchical fully correlated cost volume aggregation. A feature encoding module extracts features from two frames of images and constructs a fully correlated cost volume to model the similarity between all pixels in the two images. The hierarchical fully correlated cost volume aggregation module first divides the fully correlated cost volume into smaller blocks and spatially recombines them to allow for cost aggregation using 2D and 3D convolutions. This module performs aggregation hierarchically, performing 2D convolutions within blocks for local matching aggregation and 3D convolutions between blocks for global matching aggregation. The optical flow iterative optimization module searches for matching information from the fully correlated cost volume based on the current estimated optical flow in each iteration. This information, along with the context features of the first frame image, is input into a convolutional GRU-based update model to obtain the residual of the current optical flow. After multiple iterations, the optical flow estimation result is obtained. This invention also provides a corresponding optical flow estimation device based on hierarchical fully correlated cost volume aggregation.

The application provides a stereo matching method based on cyclic cost volume aggregation, which can gradually fuse global context and update matching details in the cost volume, thereby eliminating mismatches and ambiguities. In order to promote comprehensive cost aggregation, the application proposes a multi-peak search strategy, based on which several local cost volumes with high probability are indexed and summarized by GRU. In addition, the application also introduces a cascaded search range method, which uses a larger search range at the beginning of GRU iteration, and gradually reduces the search range with specific patterns. Based on the proposed cyclic cost aggregation method, the application constructs a new stereo matching network. A large number of experiments on the stereo matching dataset show that the method ranks first in the KITTI-12 and KITTI-15 stereo benchmark test rankings among all published methods.

This invention discloses a robust stereo matching method based on feature pyramids and attention perception, comprising: setting a feature extraction pyramid to process the input binocular stereo image, and obtaining multi-scale feature maps of the left and right view input images respectively; constructing a multi-scale cost volume pyramid using the multi-scale feature maps, and implementing feedback interaction and cross-scale cost aggregation between cost volumes of different scales; converting the cost volume pyramid into a probability value pyramid using a softmax function, and processing the probability value pyramid using a soft-argmin function to generate an initial disparity map; designing a saliency attention perception module to extract and generate attention feature maps in the shallow layers of the stereo matching network, and fusing the feature maps with the initial disparity map to obtain a refined disparity map. This invention can effectively correct erroneous matching points, restore lost image details and sharp object edges, improve the robustness and precision of disparity prediction, and generate a fine and accurate disparity map.

The invention discloses a stereo matching method based on lightweight context anchor point attention, which comprises the following steps: acquiring left and right images, and extracting features by using a lightweight feature extraction module; for the extracted left and right image features, on the basis of a grouping correlation cost body and a feature cascading cost body, a mixed cost body is constructed according to channel splicing; carrying out downsampling and upsampling operation on the mixed cost body layer by layer by adopting a multi-scale cavity volume accumulation module to obtain a cost polymer of the image; and carrying out parallax regression on the cost polymer to obtain the parallax of each position. The calculation burden is low, and the stereo matching precision requirement is ensured.

To provide a cost accounting device, a cost accounting method, and a cost accounting program for easily and accurately processing the accounting of the cost of completed construction for an ongoing project in a project where sales are based on the percentage of completion method. [Solution] The cost accounting device is equipped with a progress rate calculation processing means that calculates the progress rate for a target project and accounting month based on the final forecast data and the first cost item of the cost aggregation data, and a refund processing means that calculates the refund amount for a second cost item for a target project and accounting month by subtracting the cost amount of the second cost item from the final forecast amount of the second cost item × progress rate, and creates cost accounting data including the refund amount for the second cost item.

The application discloses a kind of substation operation scene-oriented stereovision depth estimation method and related device, it is related to substation operation technical field, method includes: based on pre-constructed illumination invariant feature enhancement module, the feature of the operation target view collected by camera is extracted;The features extracted are constructed by the method of grouping correlation cost volume, obtain several cost volume containing feature groups;The features contained in cost volume are input into the pre-constructed cost aggregation network based on differentiable least squares aggregation, obtain the aggregation result of cost volume;Disparity estimation is carried out to the aggregation result of cost volume to obtain disparity value and convert into depth information, obtain initial time difference map;The three-dimensional coordinates of operation target are obtained by combining initial time difference map and the intrinsic matrix of camera, for the positioning of operation target in substation scene.The application solves the problem of insufficient estimation accuracy and low robustness in the prior art.

The application discloses a local stereo matching method based on fusion cost calculation function and weighted guided filtering, and comprises the following steps: S1, a reference image and a matching image are obtained; S2, improved three-state Census transformation cost, enhanced gradient cost and AD-Census cost of each pixel in the reference image and the matching image are calculated, and the cost values are fused by adopting an exponential fusion algorithm; S3, an aggregation region is constructed, then cost aggregation is carried out on the aggregation region, and a cost space after aggregation is generated; S4, a winner-takes-all algorithm is adopted in the cost space after aggregation, and an initial disparity map is obtained; S5, different interpolation strategies are adopted to fill disparity for different types of matching error points, and finally, sub-pixel refinement and median filtering are carried out on the disparity map after disparity filling, so that a final disparity map is obtained. Compared with the prior art, the application overcomes the shortcomings of single cost, can generate a better disparity map, and the precision is improved by 4.9% compared with the traditional Census transformation.

The application discloses a binocular stereo matching method of mixed cost volume, equipment and storage medium, the method comprises the following steps: acquiring left and right stereo image pairs; multi-scale feature extraction is carried out on the left and right stereo image pairs, and a feature map pair under multiple scales is obtained; 3D matching cost volume and 4D matching cost volume of the corresponding scale are constructed according to the feature map pair under each scale; the 4D matching cost volume is aggregated and compressed to be equal to the size of the 3D matching cost volume, and a 4D compressed cost volume is obtained; the 4D compressed cost volume of the corresponding scale is connected with the 3D matching cost volume, and a mixed matching cost volume under multiple scales is obtained; the mixed matching cost volume is input into a 2D cost aggregation model for disparity calculation, and a disparity map is obtained. The application constructs 3D matching cost volume and 4D matching cost volume of multiple scales, obtains mixed matching cost volume under multiple scales, has high estimation performance, and can effectively reduce the operation amount through the 2D cost aggregation model.

The application discloses a binocular 3D target detection method, which comprises the following steps: left and right view features are cascaded on each disparity level to construct a disparity cost volume, and the disparity cost volume is mapped into a depth cost volume by using the inverse ratio relationship between the disparity value and the depth value; a binocular depth estimation network with 3D foreground prior embedding is constructed by a cost aggregation module, a 3D foreground segmentation module and a depth estimation module, the binocular depth estimation network is used to introduce the foreground prior knowledge contained in the 3D target detection label into the binocular depth estimation process, and a depth map suitable for the 3D target detection task is obtained; a 3D foreground segmentation mask is constructed by using the 3D target detection label, the 3D foreground segmentation mask is used to supervise the 3D foreground segmentation result, a network optimization loss function is constructed, a dynamic weight average strategy is adopted, the weight coefficients corresponding to different loss items are determined according to the change degree of the loss items, and the binocular 3D target detection network is trained. The application improves the prediction quality of the foreground region pseudo point cloud and obtains accurate binocular 3D target detection results.

The invention discloses a parallax estimation method of a structural member target fusing Transform and lightweight 3D convolution, and belongs to the field of computer machine vision. Collecting left and right view images of the structural member scene, and respectively extracting two-dimensional basic features; an improved Transform global information extraction module is utilized to capture a long-distance dependency relationship and local detail features, and global features are obtained; the two-dimensional basic features and the global features are fused, and a weighted 4D cost body is constructed in combination with YOLOv5s target detection prior and a grouping distance mapping strategy; a lightweight deformable 3D convolution cost aggregation model is adopted to optimize the cost body; and finally, a continuous disparity map with sub-pixel precision is generated based on probability distribution regression, and accurate three-dimensional positioning of the structural member target is realized. The method has excellent global information capturing capability and local detail keeping capability in a complex environment, and is outstanding in parallax estimation precision and real-time performance.