108 results about "3d surfaces" patented technology

The invention discloses an automobile part defect detection method based on multispectrum, and relates to the technical field of spectrum detection. According to the method, an optimal wave band is screened from a data source through spectral response discrimination, and the spectral texture difference between scratches and normal textures is enhanced; normal texture and suspected scratch areas are accurately divided through density clustering, a target area is focused, irrelevant data interference is reduced, and it is ensured that double-feature extraction is higher in pertinence. Further synchronously extracting texture gray level change features and 3D surface height depression features of the suspected area, and capturing dual attributes of two-dimensional texture anomaly and three-dimensional physical form depression of the scratch; and finally, through deviation calculation and relevance verification with a normal region reference feature, the abnormal degree of the double features is quantified, two types of abnormalities are ensured to correspond to the same physical region through spatial relevance, pseudo defects are eliminated, and the problem that scratches are difficult to identify based on texture gray level change and surface height depression in the prior art is solved.

The invention discloses an enhanced 3D (three-dimensional) surface reconstruction method based on 3D (three-dimensional) Gaussian Splitting. Global consistent depth priori is obtained through virtual stereo pair rendering; constructing a factor graph and introducing a cross-view geometry / luminosity consistency constraint to form local beam adjustment loss; the prior is used as a learnable parameter to be combined with 3DGS to be optimized, and meanwhile, the Pull loss is assisted to pull low-credibility pixels; and finally, multi-loss function end-to-end training is adopted. The method comprises the following steps of: in Tanksamp; the method has the advantages that F1 is equal to 0.58 and DTU Chamfer is equal to 0.48 mm on a Temples data set, the training time is only 20 min, compared with the prior art, geometric accuracy SOTA and speed magnitude improvement are achieved at the same time, and the method is suitable for VR / AR, robot and industrial measurement scenes.

A Kaleidoscopic Geometric Vision Platform (KGVP) that transforms three-dimensional (3D) machine vision through an optical system that creates multiple virtual laser projectors from a single moving component. A spindle mirror mechanism (SMM) rotates a reflective surface to sweep collimated laser beams in circular patterns. These beams strike a series of kaleidoscopic mirror facets (KMFs) arranged concentrically around the SMM in a concave configuration. Each KMF redirects the rotating beam, creating a virtual projector with a distinct origin point and sweep direction. As the SMM completes one rotation, it generates N distinct laser trajectories (where N equals the number of KMFs), each sweeping from a different virtual origin point. Event-based cameras / sensors positioned strategically around the KMFs detect laser light reflected from object surfaces with microsecond precision. The KGVP triangulates 3D surface coordinates by determining correspondence between detected light and specific virtual projectors based on precise timing information. This approach eliminates the computational complexity typically required for multi-view feature matching, as the KGVP can unambiguously identify which virtual projector created each detected event. With calibration, the KGVP can establish the exact 3D positions of all virtual projector origins and their beam trajectories and build look-up tables that optimize triangulation accuracy. The KGVP enables real-time 3D surface reconstruction with high precision while using far fewer mechanical components than conventional machine vision systems.

The present disclosure relates to the field of medical image processing and, more in particular, to a method for determining an anatomical fracture reconstruction based on one or more medical images of a broken bone, and a system configured for the same. According to an aspect the method comprises the steps of: receiving medical image data associated with the broken bone, identifying the plurality of bone fragments in the medical image data, generating a plurality of 3D surface models that virtually represent the bone fragments; registering a plurality of landmarks matching the surface models; generating a reconstructed model representing a reconstructed form of the broken bone by selecting at least two landmarks; generating a template model representing an optimal alignment of the surface models; repositioning the surface models to align at the selected landmarks set using the template model as reference; iteratively refining the reconstructed model by selecting different landmarks; changing the position of the surface models to align at the different landmarks; evaluating a quality measure that quantifies how changing the position affects the quality of the reconstructed model until a stopping condition is satisfied; and determining an anatomical reconstruction of the broken bone by comparing the position of the identified bone fragments in the medical image data with the position of the corresponding surface models in the reconstructed model.

This invention provides an automatic calibration method and apparatus for a ship panoramic surround view system based on UAV collaboration and 3D surface mapping. A virtual 3D surface projection model is established in the ship's coordinate system, and the surface normal vectors of sampling points on this model are mapped outwards to generate the UAV's flight trajectory. The UAV, equipped with an RTK positioning module, acts as a dynamic calibration target with precise three-dimensional coordinates as it flies along this trajectory, its spatial distribution naturally matching the geometry of the surface projection model. During flight, images from the UAV are simultaneously acquired by the ship's onboard camera, and pixel coordinates are extracted using a target detection algorithm. Simultaneously, the time synchronization data between the UAV and the ship's onboard RTK module is used to establish the correspondence between pixel coordinates and three-dimensional coordinates in the ship's coordinate system, minimizing reprojection errors and jointly calculating the extrinsic parameters of each camera. This eliminates the dependence on fixed planar references on shore, enabling automated calibration with precise adaptation to the 3D surface projection model in any water area.

An intraoral scanning system (102) includes a handheld intraoral scanner (104), a processor (106), and a continuous volumetric ML model (108) for generating a 3D internal geometry (120) of a tooth (306). The intraoral scanner includes a sensor (214D) for detecting NIR and visible light. The processor receives the visible light and NIR information from the sensor, generates a 3D surface model (118) of the tooth using the visible light information, and determines input parameters of a continuous volume ML model by capturing a 2D NIR image (114) of an interior region of the tooth from the NIR information. Each NIR image contains pixels (722) having a projection object. The input parameters include spatial position and viewing angle, and the projected object contains point coordinates (726, 732). The continuous volume ML model processes the input parameters to determine intensity and density values for pixels in the 3D internal geometry of the 3D surface model.

The present disclosure relates to an intraoral scanning system configured to determine a three-dimensional surface model and a plurality of focus images. The intraoral scanning system comprises: a projector unit configured to emit light onto a dental object, wherein the emitted light comprises an infrared wavelength and a structured light, the structured light comprising a first visible wavelength; a first image sensor unit comprising: a first image sensor configured to acquire first reflected light from the dental object, wherein the first reflected light comprises the infrared wavelength, and wherein the first image sensor comprises a pixel array, a microlens array arranged in front of the pixel array and configured to transmit the first reflected light to the pixel array, wherein one or more microlenses of the microlens array direct the first reflected light to one or more pixels of the pixel array; a second image sensor unit comprising: a second image sensor configured to acquire second reflected light from the dental object, wherein the second reflected light comprises the structured light; a processor unit configured to: determine three-dimensional (3D) data of the dental object based on the second reflected light, determine a three-dimensional (3D) surface model of the dental object by merging the 3D data, and determine a plurality of focus images at different focus depths into the dental object by selecting a set of pixels from the pixel array associated with each microlens of the microlens array, and wherein the processor unit is configured to align a position of each focus image of the plurality of focus images to a position in the 3D surface model based on the 3D data.

A modular, multi-functional, and reusable three-dimensional (“3D”) mounting surface for use in robotic automation to facilitate changing from one robot type to another (e.g., COTS (commercial off the shelf) robot or component) is described. A change involves removing only the surface plates without the entire mounting base plate for each product, which make the manufacture process less costly and faster to deploy. The modular design of the present invention enables a flexible mounting base plate configured to incorporate different types of robot sizes, robot cabinets, and applications. This invention is not only expandable on the three-dimensional (“3D”) tessellated surface for larger robot applications, the 3D surfaces may be combined together to form even larger robot solutions for factory automation. The present invention is reconfigurable with inserts that fit precisely in the mounting base plate.

The invention discloses a non-rigid registration method and device based on 3D surface registration and a storage medium. The method comprises the steps that a 3D image of craniocerebral CT and an intraoperative visible light face image are collected; extracting facial features of the 3D image of the craniocerebral CT by adopting a three-dimensional convolutional neural network, and constructing a CT facial feature point set; extracting facial features of the intraoperative visible light facial image by adopting a three-dimensional convolutional neural network, and constructing a visible light facial feature point set; establishing an initial corresponding relation matrix based on a standard topological structure of the CT facial feature point set and the visible light facial feature point set, and endowing each corresponding relation in the initial corresponding relation matrix with a reliability weight to obtain a face prior corresponding relation; and introducing a face prior corresponding relation as an initial parameter of the t distribution hybrid model, and outputting a registration result after iteratively optimizing the parameter. According to the method, two algorithms are combined for surgical navigation registration, so that a rapid and accurate registration effect is achieved, and the requirements of clinical surgical navigation are better met.

The present disclosure relates to a system and method for improving or refining the accuracy and / or level of detail of a 3D surface based on pixel data from a set of images, such as color data of pixels. In particular, the present disclosure relates to a 3D scanner system comprising an intraoral 3D scanner, comprising: a projector unit comprising a light source and a pattern generating element for structuring light from the light source into a pattern to be projected onto a surface of an object; and two or more cameras configured to acquire a set of images, where the set of images includes images from each camera, where each image includes an array of pixels, each pixel having a pixel color; the scanner system further includes one or more processors operatively connected to the intraoral 3D scanner configured to: generate a three-dimensional (3D) surface of the object based on the set of images obtained from the camera, where the 3D surface includes a plurality of points and / or vertices; and generating a refined 3D surface by solving the optimization problem wherein points and / or vertices in the 3D surface are relocated such that for each point or vertex, a measure based on differences in pixel colors associated with that point or vertex will be minimized.

The present disclosure provides systems and methods for multi-modal analysis of patient-specific anatomical features from medical images. The system can receive a medical image of a patient and metadata associated with the medical image indicative of a selected pathology, and automatically classify the medical image using a segmentation algorithm. The system can use an anatomical feature identification algorithm to identify one or more patient-specific anatomical features within the medical image by exploring an anatomical knowledge dataset. A 3D surface mesh model representing the one or more classified patient-specific anatomical features can be generated, such that information can be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model can be generated based on the extracted information.

The present technology relates to standardized coordinate systems for mapping three-dimensional structures. An exemplary method includes: obtaining imaging data including a representation of a left atrium of a subject; identifying anatomical features represented in the imaging data, the anatomical features including pulmonary veins that connect to the left atrium; generating a 3D surface mesh corresponding to the left atrium; determining a reference point on the 3D surface mesh based on locations of junctions of the pulmonary veins with the left atrium; establishing a reference axis based on the reference point; determining, on the 3D surface mesh and based on the reference axis, lines of longitude; determining, on the 3D surface mesh, lines of latitude along the lines of longitude; assigning coordinate values to points on the 3D surface mesh based on the lines of longitude and latitude; and generating a 2D map of the left atrium using the coordinate values.

A system and method for capturing images of a target area includes controlling an image sensor mounted on a gimbal to capture images of the target area over time (S202), and controlling the gimbal to adjust the orientation of the image sensor using an elevator algorithm (S204).

Methods and apparatuses for automating the retopologization of 3D meshes including the automated selection and adjustment of correspondence points are described. The automated selection of correspondence points may be performed to refine locations of correspondence points using a matching score that is computed based on surface normal similarity between surfaces corresponding with a candidate correspondence point on an input scan mesh and a point on a morphable model of 3D surfaces. The matching score may also take into account a distance between a candidate correspondence point on the input scan mesh and a corresponding point on the morphable model of 3D surfaces and similarities in surface features, such as similarities in surface curvature at the candidate correspondence point on the input scan mesh and the corresponding point on the morphable model of 3D surfaces.

A method for deriving a wear state of a soil interaction component of a soil processing implement of a construction vehicle. The method comprises steps of 1.) providing a geometry model regarding an assumed shape of the soil interaction component, 2.) engaging the soil by using the soil interaction component and tracking a motion for deriving tracking data of the soil interaction component, 3.) using the tracking data and the geometry model to derive an expected 3D surface model, 4.) providing visual 3D perception data of a soil area affected by the engaging, such that the visual 3D perception data and the expected 3D surface model can be referenced to one another, and 5.) comparing the visual 3D perception data with the expected 3D surface model and, based thereof, determining a deviation of an effective shape of the soil interaction component from the assumed shape.

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.

A computer-implemented method and apparatus for generating a quadrilateral grid from an image with an alpha channel. The method includes receiving an image with an alpha channel depicting a shape mapped onto a 3D surface, vectorizing the shape to create a vector path, determining corners by calculating a centroid and identifying the points, correcting a winding order to ensure a clockwise orientation, constructing a quadrilateral grid by setting sub-paths based on detected corners, resampling the sub-paths to match specified rows and columns, performing weighted interpolation across the grid using inverse square distance weighting, and outputting the generated quadrilateral grid for display on a computer device. The apparatus comprises a processor configured to execute these steps, including vectorizing the shape, determining corners, correcting the winding order, constructing and resampling the grid, performing weighted interpolation, and subdividing the 3D mesh for flexible manipulation and extrapolation.

Methods and systems are described that generate three-dimensional (3D) measurements from points in two-dimensional (2D) images. An example method includes determining an alignment between a 2D image of a dental site and a 3D surface of the dental site, determining a first point and a second point in the 2D image, projecting the first point and the second point onto the 3D surface based on the alignment between the 2D image and the 3D surface, performing a measurement of a distance between the projected first point and the projected second point on the 3D surface, and displaying at least one of a) a first visualization of the measurement on the 2D image or b) a second visualization of the measurement on the 3D surface.

This application provides a spatial intelligent non-planar reflection generation method based on surface normal guidance. Multi-layer visual representations are extracted from the original RGB image of a scene containing a non-planar reflective medium. The non-planar reflective regions are identified by combining the brightness distribution and spatial symmetry of the original RGB image. The nonlinear deformation features of the texture in the non-planar reflective regions are analyzed, and pixel-level 3D surface normal vectors and confidence distributions are predicted using the multi-layer visual representations to construct a surface normal map. A gaze vector is determined based on a camera imaging model, and geometric optics mapping operations are performed on the gaze vector, surface normal map, and confidence distribution to construct a reflection offset field. Intermediate semantic feature representations corresponding to the non-planar reflective regions are extracted, and a pre-distorted reflection feature representation is generated based on the reflection offset field and the intermediate semantic feature representation. The pre-distorted reflection feature representation, multi-layer visual representation, and initial reflective region mask are injected into the generation model for image reconstruction, generating a non-planar reflective image.

This invention provides a method and system for 3D reconstruction of animal surfaces using a shearing robot. First, the robot arm's scanning pose is determined through a Gaussian mutual information-based scanning pose inference mechanism to ensure multi-view coverage of the target surface by the depth camera. Then, the robot arm executes motion planning to acquire point clouds of the target from multiple perspectives, and the original point clouds are filtered and denoised. Next, coarse registration is performed using feature extraction and a robust algorithm to provide initial values ​​for fine registration. Based on the coarse registration, a Fast-GICP registration algorithm based on sliding window optimization is used, combined with a bidirectional frame-by-frame progressive strategy, to jointly optimize multiple frames of point clouds within a local time window, improving global registration accuracy. Finally, the multi-view point clouds are fused to generate a continuous and complete 3D surface model, achieving high-precision and robust 3D reconstruction of the animal surface.

The present disclosure provides a 3D tissue reconstruction implant having a hydrophilic implant surface and consisting of a bioabsorbable material wherein the implant surface is considered hydrophilic when evaluated using an ink test that follows ISO 8296: 2003 and adapts to the 3D surface, and wherein the implant surface is not hydrophilic when evaluated using an ink test that follows ISO 8296: 2003 and adapts to the 3D surface. Wherein the ink test comprises incubating the implant in a solution consisting of an ink having a surface tension of 72 mN / m and water for 1 minute, and wherein after the implant is incubated in the solution, the ink colored implant surface is an indication of a hydrophilic implant surface. The disclosure also provides the implant for tissue reconstruction, (soft) tissue support, tissue compatibilization and / or implant repair, and the use of the implant for reconstruction, compatibilization and / or repair of tissue and / or support of (soft) tissue, respectively, where the implant is embedded in a patient. The present disclosure further provides a method of manufacturing a tissue reconstruction implant having a hydrophilic implant surface.

In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a simulated environment. For example, a simulation may be run to simulate a virtual world or environment, render frames of virtual sensor data (e.g., images), and generate corresponding depth maps and segmentation masks (identifying a component of the simulated environment such as a road). To generate input training data, 3D structure estimation may be performed on a rendered frame to generate a representation of a 3D surface structure of the road. To generate corresponding ground truth training data, a corresponding depth map and segmentation mask may be used to generate a dense representation of the 3D surface structure.

AutoWrap Robotics is an AI-driven software platform enabling autonomous surface treatments—such as PPF, vinyl wraps, ceramic coatings, window tinting, and custom graphics—on vehicles, watercraft, and buildings. It integrates real-time 3D surface mapping, machine learning, and automated multi-tool switching to perform complex tasks with minimal human input. The system supports gantry robots, articulated arms, mobile platforms, and humanoid robots like Tesla Optimus and Figure AI, offering scalable, flexible control. Cloud-based intelligence powers fleet-wide optimization, allowing shared data to continuously improve motion paths, defect correction, and alignment. This ensures consistent quality and precision across high-volume applications, including branded fleets and commercial installations. By uniting AI-driven planning with robotic dexterity, AutoWrap Robotics delivers high-quality film and coating applications in both controlled and remote settings—without requiring custom hardware. This lowers deployment time, reduces operational costs, and positions the platform as a scalable solution for automated surface treatment at industrial scale.

A method for imaging an intraoral sample using optical coherence tomography (OCT) or ultrasound to acquire image data content from multiple scans of the sample using a hand-held, depth-penetrating scanner and reconstruct a 3D surface according to the acquired measurements. For coordinate locations on the reconstructed 3D surface, the method repeats a sequence of identifying scan data related to material at the coordinate location; computing material content according to a consensus of the identified scan data; and labeling the coordinate location according to the computed consensus for the material content. The method records the label assignments and displays, stores, or transmits the labeled, reconstructed image content.

A method includes accessing ultrasound data encoding 3D surface information of one or more body structures in a region, extracting a first 3D surface representation of the one or more body structures based on the ultrasound data, accessing non-ultrasound imaging data obtained from performing non-ultrasound imaging of the region; extracting a second 3D surface representation of the one or more body structures based on the non-ultrasound imaging data; identifying, using a deep learning algorithm, one or more regions of interest (ROI)s from the first 3D surface representation such that when aligning the first 3D surface representation and the second 3D surface representation over the one or more regions of interest (ROI)s, a spatial transformation is determined; and based on applying the spatial transformation, generating an overlay of the 3D surface anatomical information from the non-ultrasound imaging data on the 3D surface information from the ultrasound data.

Techniques for texturing three-dimensional (“3D”) surfaces using large language models (“LLMs”) are disclosed. A computing system receives information about a 3D surface. The computing system determines one or more segments of the 3D surface based on the information about the 3D surface. The computing system provides, to an LLM, a prompt including the one or more segments. The computing system receives, from the LLM, a material type associated with each of the one or more segments. The computing system determines, using a materials database, a material for each of the one or more segments based on the associated material type. The computing system renders an image of the 3D surface, in which the rendered image shows each segment of the one or more segments with the determined material applied.

Methods and systems for imaging and analysis are described. Accurate pressure ulcer measurement is critical in assessing the effectiveness of treatment. However, the traditional measuring process is subjective. Each health care provider may measure the same wound differently, especially related to the depth of the wound. Even the same health care provider may obtain inconsistent measurements when measuring the same wound at different times. Also, the measuring process requires frequent contact with the wound, which increases risk of contamination or infection and can be uncomfortable for the patient. The present application describes a new automatic pressure ulcer monitoring system (PrUMS), which uses a tablet connected to a 3D scanner, to provide an objective, consistent, non-contact measurement method. The present disclosure combines color segmentation on 2D images and 3D surface gradients to automatically segment the wound region for advanced wound measurements.

The invention relates to a technique for generating control commands for a robot for disassembling an assembly (300). A data structure of an assembly (300) which comprises a plurality of components (302; 304) is read in. The data structure comprises a digital data record with a 3D surface representation for the components (302; 304) and information regarding the position and orientation of the components (302; 304) in the assembled state. A digital geometry data record of the robot which comprises a holding element (350) is read in. A digital simulation environment for the assembly (300) in the assembled state is loaded. A holding point on each component (302;304) is determined on the basis of the read-in data structure. The holding point is used to hold the respective component (302; 304) by means of the holding element (350). By means of a trained model, a collision-free disassembly path is determined, which comprises a trajectory of the robot for the component (302; 304) from the assembled state to a storage location in the simulation environment. Control commands for controlling the robot along the collision-free disassembly path are generated.

A computer-implemented method for generating and refining synthetic video content comprises: receiving a text prompt describing a product and a markup; generating a synthetic image of the product having the markup using a generative artificial intelligence (GAI) system; validating the synthetic image against physical product specifications; mapping the synthetic image onto a 3D surface; applying inpainting techniques to remove the markup; generating an enhanced synthetic image by incorporating lighting and specular effects; generating an interactive asset by mapping the enhanced synthetic image onto the 3D surface of the product; generating using the GAI system a video frame sequence; for each frame, generating a vector data file for the interactive asset; generating a video data file based on each vector data file for each frame, and customization instructions; and transmitting the video data file and the customization instructions to a manufacturing entity for rendering of the interactive asset.

This invention discloses a pixel-by-pixel cross-medium 3D measurement method based on a ray model, belonging to the field of optical measurement technology. Addressing the technical challenge of imaging distortion caused by light refraction of the object under transparent media coverage, this method proposes a novel calibration and reconstruction framework that does not rely on precise modeling of medium parameters. By constructing a structured light projection system and utilizing imaging data of a calibration target in multiple height planes, the method independently calibrates the corresponding object-side ray equation for each camera pixel. Combined with binocular stereo matching and pixel-height mapping techniques, it ultimately achieves high-precision 3D surface reconstruction of cross-medium scenes. This invention is applicable to optical measurement scenarios with unknown or complex refractive interfaces, aiming to overcome the bottlenecks of difficult parameter solving and limited accuracy of traditional pinhole models in cross-medium environments, providing an innovative solution for 3D measurement in complex environments such as underwater and inside protective enclosures.