99 results about "3d space" patented technology

The invention discloses an image processing method and device and a storage medium. The method comprises the steps of obtaining a to-be-simulated 3D convolution model and training data; decomposing the 3D convolution model into cascading of a 3D space convolution model and a 3D time convolution model to obtain a pseudo 3D cascading convolution model; training a pseudo 3D cascade convolution modelby using the training data, and obtaining parameters of a 3D spatial convolution model and a 3D time convolution model; converting the 3D space convolution model and the 3D time convolution model intoa 2D space convolution model and a 2D time convolution model; setting a feature rearrangement rule for the 2D spatial convolution model and the 2D time convolution model; mapping model parameters ofthe 3D spatial convolution model and the 3D time convolution model into parameters of a 2D spatial convolution model and a 2D time convolution model to obtain a 2D cascaded convolution model; and performing convolution operation on the image by using the 2D spatial convolution model and the 2D time convolution model. By means of the mode, image processing conducted through 3D convolution operationcan be achieved through the 2D convolution model.

Provided herein are systems for determining a location of a gross target volume of a patient. In some examples, systems can include one or more processors that are configured to obtain image data associated with a plurality of images of a lesion of a patient. For each image, the one or more processors can be configured to backproject points representing the lesion into the 3D space to determine a plurality of distribution confidence values for a subset of voxels within the three-dimensional space. The one or more processors can be configured to determine a three-dimensional confidence distribution based on confidence values from the plurality of distribution confidence values corresponding to each voxel of the 3D space and determine a position of the lesion within the 3D space based on the 3D confidence distribution.

The application discloses an intelligent path planning method and system for a mechanical arm spray head of a deicing vehicle, and can realize automatic and accurate deicing on a complex three-dimensional surface of an airplane. The method comprises the following steps: target surface extraction and reconstruction are performed on a deicing area of the surface of the airplane to generate a continuous three-dimensional surface; the three-dimensional surface is mapped to a two-dimensional parameter plane by surface parameterization; a 2D waypoint sequence is generated on the two-dimensional parameter plane in combination with the effective spraying width of the nozzle; 3D space inverse mapping is performed based on the 2D waypoint sequence, the spray head posture of the deicing vehicle is aligned based on the result of the inverse mapping, and the three-dimensional target posture of the nozzle at each waypoint is obtained; the target posture of the nozzle is converted into specific angle instructions of each joint of the mechanical arm of the deicing vehicle; on the basis of the joint angle instructions, joint trajectory smoothing and S-type speed planning are performed, and finally the action path of the spray head of the mechanical arm of the deicing vehicle is generated.

A computer-implemented method is provided for acquiring a set of x unique tracking constellations in a predefined 3D space, each constellation for a corresponding wearable tracking device used to track a wearer in a 3D environment. Each unique tracking constellation includes a predefined number n tracking objects. A set of y discrete available locations is defined for locating the tracking objects within the predefined 3D space. A minimum number m of tracking objects is defined, wherein the minimum number is less than or equal to the predefined number n of tracking objects. For each unique tracking constellation, a unique discrete available location from the set of y discrete available locations is assigned to each tracking object of the tracking constellation to acquire the unique tracking constellation. The assigned location for any minimum number m of tracking objects in each unique tracking constellation is unique within the set of x unique tracking constellations.

This invention discloses a pose estimation method for industrial parts that integrates neural implicit representation and MGC-SR anti-reflection constraints. The method first acquires RGB-D images and a 3D CAD model of the part to be assembled; it then extracts multimodal features using a pose prediction model based on neural implicit representation and generates an initial 6 DoF pose of the part in the camera coordinate system; it converts the depth image into an observation point cloud using camera intrinsic parameters and performs coarse alignment with the CAD model point cloud in 3D space; it constructs an anti-reflection weighted model (MGC-SR) based on multi-source geometric confidence, and introduces an adaptive weight allocation strategy based on normal consistency and depth confidence to address noise interference in industrial high-reflectivity scenarios. Using Lie group and Lie algebra theory, it iterative fine-tuning minimizes the point cloud registration error in the tangent space, outputting the final high-precision pose. This invention combines the zero-shot generalization of deep learning with the physical accuracy of 3D geometric registration, effectively solving the problems of pose jitter and insufficient accuracy of industrial metal parts in high-reflectivity, low-texture environments, and has high engineering application value.

This application provides a method, apparatus, device, medium, and program product for determining a vehicle cleaning path. The method maps the 3D point cloud data of the vehicle to be cleaned to a 2D space according to the Z-axis and X-axis projection directions to obtain an unobstructed area; it then segments the unobstructed area to obtain a list of cells and a scan line corresponding to each cell; using a path connection algorithm, it generates candidate cleaning paths in the 2D space based on the cell list and the scan line corresponding to each cell; finally, it maps the candidate cleaning paths to a 3D space using bivariate spline interpolation to obtain the target cleaning path. This application avoids the redundant path problem of traditional segmentation methods by mapping the vehicle's 3D point cloud data to a 2D space and then performing region decomposition and path planning; finally, it maps the paths from the 2D space back to the 3D space using bivariate spline interpolation, ensuring the continuity and coverage integrity of the cleaning path.

This invention discloses a PCB characteristic impedance simulation optimization method and system based on 3D modeling, relating to the field of high-speed intelligent circuit technology. The method involves acquiring measured material parameters and process deviations of the PCB to generate twin parameter vectors; constructing a local 3D subdomain model; concatenating the local 3D subdomain model with the manufacturing twin dataset to obtain multi-dimensional physical feature vectors, which are then processed to obtain a sequence of key influencing factors; processing the key influencing factor sequence using an impedance prediction proxy model to obtain the time-domain reflection impedance curve and the corresponding coordinates of 3D spatial impedance abrupt change points; adjusting the coordinates of the 3D spatial impedance abrupt change points based on an impedance reshaping strategy to obtain optimal 3D compensation parameters; reconstructing the 3D simulation model using the optimal 3D compensation parameters to obtain updated PCB manufacturing data; and outputting impedance control parameters through a target verification rule engine to accurately locate the coordinates of impedance abrupt change points in 3D space, thereby improving design efficiency and quality.

This invention provides an atomic-level electron tomography 3D reconstruction method. Its core is to match the simulated projection of an atomic model with experimental images through iterative optimization. The method first constructs a 3D space based on the sample contour and initializes the atomic model parameters. Then, it acquires experimental images from multiple angles and their imaging parameters. In each iteration, a simulated projection image is generated based on the current atomic model and imaging parameters, and the difference between it and the corresponding experimental image is calculated as the loss value. An optimization algorithm dynamically adjusts atomic parameters (position, size, etc.) and imaging parameters (such as focus distance and aberrations) to minimize the total loss. This process includes intelligent simplification steps, which can merge or delete atoms to optimize model complexity. Finally, using the converged high-precision atomic model, 3D reconstructed images with arbitrary viewpoints and depths can be generated. This method can achieve high-precision atomic-level 3D reconstruction without completely relying on 360-degree scan data, helping to reduce data acquisition costs.

Aspects concern a virtual reality control device comprising one or more sensors configured to sense sensor data allowing determining the 3D position of the virtual reality control device in 3D space, at least one rotary wheel switch; and an output interface configured to output the sensor data from the one or more sensors and information about a rotation of the at least one rotary wheel switch.

The systems and methods display, by at least one processor on a first portion of a real-world environment visible on a display of a user device, one or more virtual objects at a first virtual coordinate in three-dimensional (3D) space. The systems and methods receive a request to move the one or more virtual objects to a second virtual coordinate in 3D space. The systems and methods in response to receiving the request, maintain display of the one or more virtual objects at the second virtual coordinate as a second portion of the real-world environment is visible on the display of the user device.

The present invention relates to systems and methods for determining a location of a gross target volume of a patient. In some examples, a system can include one or more processors configured to obtain image data associated with a plurality of images of a lesion of a patient. For each image, the one or more processors can be configured to backproject points representative of the lesion into a 3D space to determine a plurality of distribution confidence values for a subset of voxels within the three-dimensional space. The one or more processors can be configured to determine a three-dimensional confidence distribution based on the confidence values from the plurality of distribution confidence values corresponding to each voxel of the 3D space and determine a location of the lesion in the 3D space based on the three-dimensional confidence distribution.

The application discloses a face anti-counterfeiting method and device and a storage medium. The face anti-counterfeiting method comprises the following steps: acquiring a to-be-detected image collected by a 2D monocular camera; acquiring a depth factor corresponding to each pixel coordinate point of the to-be-detected image through a preset face depth model; converting the to-be-detected image into a 3D space information image according to the depth factor and preset camera parameters; judging whether the converted 3D space information image is a plane false face; and if the 3D space information image is not a plane false face, judging whether the to-be-detected image is a three-dimensional false face through a preset three-dimensional anti-counterfeiting model. Thus, the face depth model and the three-dimensional anti-counterfeiting model are used to realize two-dimensional and three-dimensional anti-counterfeiting of the image through the 2D monocular camera. The hardware requirement for the camera is reduced without the action cooperation of the verifier, the face verification efficiency is improved, and the face anti-counterfeiting accuracy is improved.

Disclosed herein is a three-dimensional (3D) display apparatus and method based on a low-coherence light source. The 3D display apparatus based on a low-coherence light source may include a first modulation panel for generating and displaying two-dimensional (2D) amplitude information using a self-emissive low-coherence light source and a second modulation panel for converting the 2D amplitude information of the first modulation panel into voxels in a 3D space by modulating the 2D amplitude information into phase information.

A tennis 3D trajectory reconstruction method and device based on monocular vision mainly include: real-time acquisition of monocular motion video; extracting the 2D pixel bounding box of the tennis in each frame, and splicing out the complete tennis 2D motion trajectory; taking the tennis space motion physical model containing gravitational acceleration and air resistance as the constraint condition, constructing the re-projection error function from the 3D space coordinate to the 2D pixel coordinate, taking the initial 3D position and the initial velocity vector of the tennis as the joint optimization variable, and adopting the nonlinear optimization algorithm for iterative fitting, so that the re-projection error between the coordinates of the theoretical 3D trajectory re-projected to the 2D plane and the actual tennis 2D motion trajectory is minimized, thereby inversely solving the accurate 3D trajectory. Through the 3D reconstruction method based on 2D trajectory fitting and projection iterative optimization, the tracking and depth estimation problem of high-speed tennis in the terminal with limited computing power can be effectively solved.

This application discloses a multimodal data collaborative intelligent detection and 3D localization method for maritime vessels, comprising: fine preprocessing of collected multi-source heterogeneous data and preliminary target detection; dynamic weighted fusion of the preprocessed multi-source heterogeneous data, combined with depth data from lidar, to achieve accurate coordinate recovery of the vessel target in 3D space; and prediction and updating of the motion state of the identified target based on a multi-tracking mechanism collaborative strategy. By fully utilizing the rich texture information of visible light images, the anti-interference capability of thermal imaging, and the precise spatial depth data of lidar through multimodal data perception and preliminary processing, heterogeneous data deep fusion and 3D localization, and advanced multi-target continuous tracking and state estimation, this method integrates a multi-tracking mechanism collaborative strategy to predict and update the motion state of the identified target, effectively addressing the problem of tracking interruption caused by nonlinear motion or temporary occlusion of maritime targets.

This invention relates to the field of tunnel detection and identification technology, specifically a method for detecting defects in water diversion tunnels based on a 3D model. The method includes: collecting laser point cloud data, panoramic image sequences, and ground-penetrating radar (GPR) profile data of the target water diversion tunnel section to form a multi-source detection data set. A 3D mesh model of the tunnel structure is reconstructed using the laser point cloud data. The panoramic image sequence is mapped onto the model to generate a textured 3D model. Abnormal signal segments from the GPR profile data are identified to obtain a set of radar anomaly depth markers, which are then projected onto the textured 3D model to construct a 3D detection model with defect markers. Candidate defect regions are extracted through region growing and segmentation. The opening width and extension length of each candidate defect region are quantified. Based on geometric features, the defect type is classified, and a defect detection report is output. This method achieves the fusion and correlation of multi-source detection data in 3D space, completing automatic defect region segmentation and geometric feature quantification.

The application provides an automatic driving visual perception feature extraction method and device, raw images are collected based on multiple camera sensors around a vehicle body, and the surrounding space is divided into multiple voxel units with the vehicle body as the center; a raw image feature map of raw image features extracted by a backbone network is constructed, a preset attention module containing a time sequence attention module and a space attention module is constructed, each voxel is taken as a basic prediction unit, past multi-time features are fused through the time sequence attention module, features of the raw image feature map mapped on voxels in a three-dimensional space are mined through the space attention module, features in the 3D space are recovered from the 2D image, memories of multiple time points are fused, and spatial features with better representation ability are mined to meet learning requirements of downstream tasks of automatic driving.

This application discloses a data processing method, apparatus, device, medium, and product. The method includes: after acquiring a target image for describing a target object, first predicting the projection coordinates of vertices in the 3D mesh of the object onto the target image based on the image features of the target image, so that the projection coordinates can indicate the correspondence between pixels in the target image and vertices in the 3D network, thereby enabling the projection coordinates to describe some characteristics of the predicted 3D mesh to a certain extent, such as vertex distribution characteristics and edge characteristics; then predicting at least one parameter based on the image features and the projection coordinates, so that these parameters can represent the driving parameters required when generating the 3D mesh, so that these parameters can be used to drive a 3D deformable model to obtain the 3D mesh, so that the 3D mesh can describe the state of the object in 3D space, thus realizing the generation of a 3D network based on a 2D image.

A system, method, medium, and implementation for registering an ultrasound probe in laparoscopic ultrasound surgery, and its applications are disclosed. The two-dimensional (2D) position of the ultrasound probe in a 2D laparoscopic (LP) image is detected, wherein the LP camera has been previously calibrated in three-dimensional (3D) space. Based on the detected 2D position of the ultrasound probe and the ultrasound model used for the probe, the 3D pose of the deployed ultrasound probe is estimated and registered in 3D space.

A rendering method, apparatus, device, and storage medium for a 3D model are disclosed, relating to the field of computer graphics technology. The method includes: acquiring vertex position data of a 3D model, wherein the vertex position data indicates a first position of at least one vertex of the 3D model in 3D space; projecting at least one vertex from 3D space to screen space according to camera parameters of a virtual camera in 3D space, obtaining projection point position data, wherein the projection point position data indicates a second position of the corresponding projection point of at least one vertex in screen space; predicting projection point color data corresponding to a preset lighting effect of the 3D model in 3D space using a light gradient model based on the projection point position data and camera parameters; and rendering the 3D model using the projection point position data and projection point color data to obtain a rendered image of the model. This application enables automatic rendering of lighting effects of 3D models, improving the rendering efficiency of lighting effects.

This invention belongs to the field of vehicle automation technology, specifically relating to a machine vision-based method for locating target objects in vehicles. A camera on the vehicle acquires scene images of the target object, a neural network is used to obtain the center pixel coordinates of the target object in the scene image, and the distance between the vehicle and the target object is calculated using the camera's height from the ground and the camera's intrinsic parameter matrix. This invention is mainly applied to target object location during the retrofitting of old vehicles. By combining target object identification in a neural network with camera homography calibration, the relative position of the target object in 3D space is accurately calculated, enabling precise target object location even in complex scenes.

This application provides a data processing method, apparatus, computer device, and readable storage medium, relating to the field of autonomous driving. The method includes: acquiring an image data packet of an autonomous vehicle's estimated initial trajectory and driving environment, wherein the image data packet includes multiple image data points acquired by the autonomous vehicle's camera at different positions along the estimated initial trajectory; performing image matching on the multiple image data points, determining the coordinates of different image feature points in 3D space based on the image matching results, obtaining multiple 3D space points; constructing a reprojection error function based on the camera's extrinsic parameters and the 3D space coordinates of the 3D space points; and optimizing the reprojection error function to obtain target camera extrinsic parameters and target 3D space coordinates that minimize the reprojection error function.

Systems and methods are provided for verifying a user. A method include receiving a verification challenge request including data indicative of a plurality of sensors. The method includes determining, based on the data, one or more verification challenges which are encrypted and delivered to a secured application executing on the user device. The method includes causing the secured application to lock at least one sensor that is used for the verification challenge and to process data from the at least one sensor. The method includes determining that the user is verified based on the processed data from the at least one sensor, and causing an application executing on the user device to provide the user access to at least one resource of the application.

Disclosed in the embodiments of the present disclosure are a method for keyboard input in a three-dimensional (3D) space, and a head-mounted display device, a medium and a product. A specific embodiment of the method comprises: in response to a typing operation performed on an associated physical keyboard being detected, determining a candidate word list on the basis of at least one key position corresponding to the typing operation, wherein an input cursor is displayed in a window corresponding to the typing operation, and the window is displayed in a 3D space of a head-mounted display device; determining a spatial dimension of the window corresponding to the typing operation; in response to it being determined that the spatial dimension represents a two-dimensional window, generating candidate word bar position information on the basis of two-dimensional position information of the input cursor in the window; and on the basis of the candidate word bar position information, displaying a candidate word bar in the window, wherein at least one candidate word in the candidate word list is displayed in the candidate word bar. The embodiment can support input in a 3D space, and can enable a word selection box to dynamically focus near an input box.

The present application relates to the technical field of image reconstruction, in particular to a kind of imaging method of 3D space position information and 2D color texture information fusion, comprising the following steps: according to 2D color texture information, extract 2D color texture information internal gray level co-occurrence matrix pixel parameter, generate two-dimensional color mutation boundary, obtain 3D space position information accompanying space isomorphism determination item.In the present application, three-dimensional feature distribution skeleton coordinate set is used to participate in the solution of curved surface control point, so that the spatial curved surface reconstruction has continuity and geometric consistency;Finally, by converting two-dimensional color mutation boundary coordinates into Lagrange multiplier constraint variable and embedding into the solving process, the boundary constraint directly participates in the three-dimensional curved surface generation process, and then the texture pixel value is mapped to the external connection surface of three-dimensional curved surface grid node set, to realize the synchronous expression of space structure and texture information.

Ways to facilitate multimodal interaction and communication for users are provided. A personal assistant system for a user includes a portable device equipped with a three-dimensional (3D) camera for generating images of 3D space and a propulsion system for self-propulsion. The personal assistant system also comprises a speaker, a microphone, and a control system interfacing with these components. The control system features a navigation module for controlling the propulsion system, an audio recognition module for converting audio from the microphone, and an image recognition module for processing images from the 3D camera. A processor transforms these formats into a processor-output, while an artificial intelligence (AI) module analyzes the formats to identify actionable commands and convert them into processor-output. An output generating module then converts the processor-output into a user-friendly output. Use of the personal assistant system enhances user convenience, accessibility, and engagement in educational, professional, and personal pursuits.

Methods and systems for executing a Gaussian online splatting model for simultaneously localizing and mapping a surrounding 3D space are disclosed. The model is configured to receive an image-based data sample representing a first field of view of the 3D space and, using a Gaussian 3D map of the model, to render a new image-based data sample representing a new field of view distinct from the first, as well as to render the corresponding speech features. By incorporating a hierarchical encoder and a contrastive speech-image pre-training model (CLIP model) into the architecture of the Gaussian online splatting model, the overall architecture is configured to operate in near real-time.