19 results about "3d image reconstruction" patented technology

The invention discloses a power distribution area load state monitoring method and system based on stereo portrait analysis, relates to the technical field of urban power grids, and effectively improves the positioning precision and response speed of a load fluctuation area by introducing a three-dimensional image reconstruction and dynamic partitioning mechanism; a graph neural network and space-time long-short-term memory network modeling method is combined, so that the trend perception capability of the load state evolution process is enhanced; further, multi-source fusion recognition of abnormal behaviors is realized through association matching of the image change track and the load feature sequence, and the monitoring accuracy and sensitivity are effectively improved; based on risk index assessment and a map visualization expression mode, an abnormal area positioning result is visual and transparent, and intelligent early warning and decision assistance are supported; the method has the advantages of high data fusion capability, high risk identification precision, high visual interactivity and the like, and the intelligent and practical level of load state monitoring of the power distribution area is remarkably improved.

This invention discloses a method for identifying the connection location of underground dissimilar pipelines based on 3D image reconstruction, belonging to the field of location recognition technology. It addresses the problems of distorted spatial structure representation and insufficient accuracy in determining connection locations. By segmenting the underground pipeline, fusing ground-penetrating radar reflection data and electromagnetic detection results, abrupt gradient coefficients are calculated to mark potential material abrupt change segments. 3D scan data of the marked segments are retrieved, and combined with scan density and segment structure information, connection candidates are generated. Enhanced scanning is performed on the candidates, and the pit landing point is determined through spatial coarseness features. The existence of a connection interface is determined based on local point clouds, regional point clouds are extracted, and axis fitting is performed on the regional point clouds to obtain 3D misalignment data of the pipelines on both sides. The construction area is divided based on the soil obstacle density around the connection interface, and the connection center point is output based on misalignment constraints. This significantly improves construction accuracy and reduces the risk of blind excavation.

This invention relates to the field of intelligent inspection and 3D visual reconstruction technology for automotive parts, specifically a method for 3D image reconstruction of lightweight internal high-pressure molded parts for automobiles. The method includes: acquiring a sequence of structured light images, multispectral reflectance texture images, and a priori simulation model of the target object, and completing spatial coordinate registration; dynamically allocating edge extraction weights based on the local curvature gradient of the 2D image to generate a non-uniform density 3D point cloud; constructing a surface reconstruction energy function using surface reflectance variation data as a penalty term, fitting the point cloud to the 3D surface, and generating a target 3D mesh model; fusing 3D geometric features, 2D texture features, and the priori simulation model, and outputting the wall thickness reduction rate and residual stress distribution via a graph neural network; generating and overlaying a risk heat map, and outputting the evaluation decision results. This invention extends from geometric reconstruction to risk semantic reconstruction, improving the comprehensiveness and reliability of online inspection of internal high-pressure molded parts.

This invention relates to the field of 3D image reconstruction technology, and more specifically, to a 3D image reconstruction method and system based on coordinate attention mechanism and Unet. The key technical points include the following steps: S1, providing a 3D image reconstruction system based on coordinate attention mechanism and Unet; S2, obtaining a 3D geometric image of the target using a deep learning 3D image reconstruction method on the 3D image reconstruction system. This invention has the advantages of improving the efficiency of 3D image reconstruction and increasing the modeling accuracy of complex and irregular objects.

To reduce charging artifacts in electron microscopy, a notched ring of sputterable material can be situated about a sample surface. An ion beam can be directed through a notch at to sputter the sputterable material onto the sample surface. Sputtering can be performed after low-angle focused ion beam (FIB) milling at the same sample tilts. The sample can be rotated about an axis and sputtering performed at multiple rotation angles. Upon sputtering of the conductive coating, the sample can be reoriented and imaged. These steps can be repeated to produce a 2D image stack for 3D image reconstruction.

The application relates to a 3D-vision-based sole and vamp adhesive contour line marking device and method, and relates to the field of shoe processing.The device comprises a fixing support, a pressing module, a 3D-vision module, a line marking module and a control system.The pressing module comprises a rotating part arranged on the fixing support, a supporting part arranged above the rotating part and a pressing part arranged on the fixing support and above the supporting part.The 3D-vision module comprises a 3D-vision sensor arranged on the fixing support.The line marking module comprises a line marking part, a first moving assembly for driving the line marking part to move in the vertical direction and a second moving assembly for driving the line marking part to move in the direction of approaching or moving away from the supporting part.The control system is used for carrying out 3D image reconstruction according to the 3D-vision module and calculating the adhesive contour line when the pressing module works, and controlling the line marking module to work according to the adhesive contour line.The application has the effects of improving the line marking accuracy and quality and realizing intelligent production.

An intelligent seedling breeding device with disease detection function, characterized in that it comprises: a receiving container seat (1) equipped with a spray system; a drive frame (2) rotatably connected between the receiving container seat (1); a drive motor (3) equipped with a regular on / off control module, which is fixedly connected to one side of the receiving container seat (1) and connected and fixed to the drive frame (2); a breeding box (4) equipped with a counterweight column, rotatably connected between the drive frame (2); a rotation sleeve (5) equipped with a counterweight ring, rotatably connected outside the drive frame (2); a near-infrared imaging and 3D image reconstruction module (6) fixedly connected to one end of the rotation shell (5); an air-permeable, tempered glass cover (7), on the side wall of which an image display module is arranged, which is arranged as a symmetrical structure outside the receiving container seat (1) in an articulated manner.

The invention relates to a method for tracking a focus position in real time by a monocular endoscope and dynamically displaying a focus image in a three-dimensional image reconstruction form based on deep learning. The method comprises the following steps: 1, constructing a binocular endoscope camera system; 2, constructing a convolutional neural network based on a Mnas network; 3, using a ScanNet data set and other data sets to carry out a first round of training on the convolutional neural network based on the Mnas network; 4, using the collected monocular video and the corresponding calculation depth map as a training data set, and performing a second round of training on the convolutional neural network based on the Mnas network; and 5, inputting an image acquired by a monocular endoscope software camera system into the convolutional neural network based on the Mnas network in a video streaming media mode, outputting a three-dimensional image, and tracking the track of the monocular endoscope on the three-dimensional image in real time. The invention not only provides a three-dimensional reconstruction method which is simple and convenient to operate, but also is convenient for doctors to find and more specifically observe lesions, and the operation efficiency of the doctors is improved.

This invention belongs to the field of computer vision technology and discloses a data synthesis and enhancement method based on digital twins and generative AI. It constructs a digital plant model through 3D image reconstruction, utilizing multi-view images and computer vision technology. A dataset is synthesized based on the 3D model. Two-dimensional images and annotations are obtained through 3D graphics software design and rendering. The synthesized dataset is then tested, and a 3D plant model is generated using generative AI. The synthesized data is used for training. This invention utilizes computer-generated data enhancement to solve the problem of insufficient training data for deep learning algorithms such as image segmentation. Furthermore, it enhances the synthesized data using generative artificial intelligence and digital twin technology. It constructs a digital model of a real-world plant using 3D reconstruction technology and generates a 3D digital plant model using generative AI technology. This method of generating synthesized data solves the problem of difficulty in obtaining training data in image segmentation tasks.

The invention relates to an optical tomography scanning system (1) for 3D imaging of an object (100), for example an experimental rodent or a patient, using Raman scattered light from the object. A laser (1) and an optical configuration (2) for fixating the object and arranged for rotation (Θ) is provided, and an optical receiving part (3) receives Raman scattered light. The optical receiving part has a detector array (35) for receiving the Raman scattered light in two spatial dimensions (X, Z), and optical guide means (37) convey Raman scattered light to a spectrometer (38), where multiple spectra are recorded for the two spatial dimensions for each relative rotational position (Θ) of the object. A 3D image reconstruction of the object based on the multiple spectra at a plurality of relative rotational positions (Θ) of the object enables 3D tomographic imaging of the object, such as an experimental rat.

The invention provides a 2D-to-3D image reconstruction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an original 3D bone segmentation image corresponding to a 3D CT image, calculating and aligning the 3D bone segmentation image according to the original 3D bone segmentation image, a real projection parameter and a fixed projection parameter, and obtaining a 3D-to-3D image; the original 3D bone segmentation image is mapped through the real projection parameter to obtain a 2D real projection positive and lateral position X-ray image, and the 2D real projection positive and lateral position X-ray image is mapped through the fixed projection parameter to obtain a 2D fixed projection positive and lateral position X-ray image, and the 2D fixed projection positive and lateral position X-ray image is mapped through the fixed projection parameter to obtain the 3D bone segmentation image. Therefore, the change of the real projection parameters is converted into the change of the pose and the form of the shot object under the fixed projection parameters; and inputting the 2D real projection positive and lateral X-ray image into the trained three-dimensional reconstruction model to obtain a predicted 3D aligned bone segmentation image, and calculating a real 3D bone segmentation image according to the predicted 3D aligned bone segmentation image, thereby improving the accuracy of model prediction.

A 3D scanner system includes a scanner and a processor. The scanner includes a wand comprising a distal end configured for insertion into an oral cavity, wherein the wand comprises an opening in a sidewall of the distal end; and at least one of a window arranged in the opening of the wand or a sleeve mounted on an outside of the wand such that the sleeve covers the opening. The processor is configured to receive 2D images generated by the scanner, the 2D images comprising a) captured structured light reflected off of an intraoral object and b) captured additional light arising from reflections from at least one of the window or the sleeve; and perform 3D image reconstruction using the captured structured light in the 2D images while excluding the captured additional light in the 2D images based on processing of the 2D images using one or more algorithms.

The invention discloses a 3D image reconstruction method for deformation caused by composite insulator defects, and the method comprises the steps: firstly carrying out the laser dislocation speckle test of a to-be-tested composite insulator, and obtaining a speckle image; then image data of the speckle images are extracted, the image data of the speckle images are input into a preset deformation extraction model, the deformation extraction model outputs deformation data corresponding to the to-be-detected composite insulator, and the deformation extraction model is constructed based on a DeepLabV3 + network model and a residual module; and finally, according to deformation data output by the deformation extraction model, generating a 3D reconstruction image corresponding to the to-be-detected composite insulator. Therefore, the improved deformation extraction model is utilized, and the deformation extraction model directly learns the deformation information after phase unwrapping, so that information loss in an image processing intermediate process is reduced, and the defect detection precision of the composite insulator is improved.

This application provides a multi-camera-based 3D image reconstruction method, apparatus, device, and medium, relating to the field of image processing technology. In this application, firstly, multiple original 2D images are acquired, and original internal calibration parameters and original external parameters are determined, as well as an original sparse 3D model. Secondly, distortion correction is performed on the original 2D images and original internal calibration parameters to obtain distorted 2D images and distorted internal calibration parameters. Then, during the optimization process of the original sparse 3D model, the distortion correction internal calibration parameters of each camera are fixed, and the relative positional relationships represented by the original external parameters corresponding to each camera are constrained and fixed to form a target sparse 3D model. Finally, based on each distorted 2D image, the target sparse 3D model is densified to form a target dense 3D model. Based on the above, the problem of relatively low accuracy in 3D image reconstruction in existing technologies can be improved.

The invention relates to a binocular three-dimensional image reconstruction and display method based on self-supervised deep learning, which can track a focus position and emphatically display a focus image. The method mainly comprises the following steps: constructing a binocular endoscope camera system; constructing a convolutional neural network based on an MVS framework; using a ScanNet data set and other data sets to carry out a first round of training on the convolutional neural network based on the MVS framework; using the collected binocular image and the corresponding calculation depth map as a data set of a second round of training, and carrying out the second round of training on the convolutional neural network based on the MVS framework; and inputting an image acquired by the binocular endoscope software camera system into the convolutional neural network based on the MVS framework, outputting a three-dimensional image, and tracking the trajectory of the binocular endoscope on the three-dimensional image in real time. The invention not only provides a three-dimensional reconstruction method which is simple and convenient to operate, but also facilitates searching and more specific observation of a focus by a doctor, and is expected to improve the operation efficiency of the doctor.

The invention provides a three-dimensional image establishment method and related equipment, and the method comprises the steps: converting a target image into an initial three-dimensional image, and determining the certainty degree of a first Gaussian primitive according to the attribute parameter of the first Gaussian primitive in the initial three-dimensional image; according to the determinacy of each first Gaussian primitive in the initial three-dimensional image, a space body is determined, and the space body is composed of at least one second Gaussian primitive; changing the initial shooting view angle of the space body to obtain a candidate shooting view angle, and generating a target view angle image of the space body under the candidate shooting view angle; according to the target visual angle image and an initial visual angle image of the space body under the initial shooting visual angle, a second Gaussian primitive in the initial three-dimensional image is reconstructed, a target three-dimensional image is obtained, and the determination degree of the reconstructed second Gaussian primitive is larger than or equal to a preset threshold value. According to the invention, the image quality of the three-dimensional image is improved.

This invention provides a light-field microscope 3D image reconstruction system and method integrated with an event camera, belonging to the field of optical technology. This system synthesizes raw light-field data into event data for network training. The data is then input into an event image reconstruction network module capable of converting a continuous event stream into a series of intensity images to produce a light-field event image. The 3D image reconstruction network module then generates a 3D reconstruction of the target area. This system provides a new technical reference for improving the quality and resolution of 3D reconstruction using light-field microscopes.

This surgical simulation method and system, based on 3D image reconstruction and surgical navigation, relates to the fields of biomedical engineering and virtual reality processing software. The conversion of patient medical image data into a 3D model provides a precise spatial information foundation for surgical simulation and generates multiple optional surgical navigation schemes, fully considering the feasibility of different incision locations. During virtual reality simulated surgery, dynamic display based on the user's actual perspective greatly enhances the realism and immersion of the simulation. When an operation fails, not only can the entire process of the failed operation be replayed, but alternative solutions can also be intelligently recommended, significantly improving the surgeon's learning efficiency and surgical skills. By combining image reconstruction, multi-scheme navigation, and intelligent feedback, the accuracy, flexibility, and effectiveness of surgical simulation are greatly improved.

The invention discloses a sparse view NCT three-dimensional image reconstruction method based on a neutron imaging technology, and relates to the technical field of neutron imaging, the sparse view NCT three-dimensional image reconstruction method is applied to a neutron computer imaging system, the neutron computer imaging system comprises a ray generation subsystem and a neutron imaging subsystem, and the method comprises the following steps: the ray generation subsystem generates a target ray; the neutron imaging subsystem acquires a two-dimensional original image based on the target ray; and the neutron imaging subsystem performs three-dimensional image reconstruction on the two-dimensional original image by using a sparse view NCT three-dimensional reconstruction algorithm to obtain a sparse view NCT three-dimensional image reconstruction result. According to the Os-SART-PDTV algorithm, the advantages of the Os-SART iterative algorithm and the first-order prime-dual algorithm with total variation denoising are combined, the two algorithms have partial complementarity, noise can be more effectively removed, artifacts can be more effectively inhibited, the detail structure of the image can be more effectively reserved in sparse view NCT three-dimensional reconstruction, and the reconstructed image is clearer.