79 results about "Scale estimation" patented technology

Methods, systems, and devices for wireless communications are described. The described techniques provide for a user equipment (UE) to perform receiver-side noise statistics (Rnn) estimation and for an extended reality (XR) device to perform log likelihood ratio (LLR) scaling estimation. For example, the UE may transmit a first set of reference signals to the XR device. The XR device may sample the reference signals and transmit an indication of quantized samples such that the UE may perform Rnn estimations and generate a pre-equalization matrix. The UE may transmit a set of pre-equalized reference signals to the XR device such that the XR device may estimate LLR scaling coefficients. In some examples, the UE may determine the pre-equalization matrix by performing a filtering of reference signal measurements. The UE may apply whitening to account for momentary interference, and may indicate a whitening matrix to the XR device.

Methods, systems, and devices for wireless communications are described. The described techniques provide for a user equipment (UE) to perform receiver-side noise statistics (Rnn) estimation and for an extended reality (XR) device to perform log likelihood ratio (LLR) scaling estimation. For example, the UE may transmit a first set of reference signals to the XR device. The XR device may sample the reference signals and transmit an indication of quantized samples such that the UE may perform Rnn estimations and generate a pre-equalization matrix. The UE may transmit a set of pre-equalized reference signals to the XR device such that the XR device may estimate LLR scaling coefficients. In some examples, the UE may determine the pre-equalization matrix by performing a filtering of reference signal measurements. The UE may apply whitening to account for momentary interference, and may indicate a whitening matrix to the XR device.

Methods, systems, and devices for wireless communications are described. The described techniques provide for a user equipment (UE) to perform receiver-side noise statistics (Rnn) estimation and for an extended reality (XR) device to perform log likelihood ratio (LLR) scaling estimation. For example, the UE may transmit a first set of reference signals to the XR device. The XR device may sample the reference signals and transmit an indication of quantized samples such that the UE may perform Rnn estimations and generate a pre-equalization matrix. The UE may transmit a set of pre-equalized reference signals to the XR device such that the XR device may estimate LLR scaling coefficients. In some examples, the UE may determine the pre-equalization matrix by performing a filtering of reference signal measurements. The UE may apply whitening to account for momentary interference, and may indicate a whitening matrix to the XR device.

The invention relates to the technical field of unmanned aerial vehicle formation, in particular to an unmanned aerial vehicle cooperative control method based on visual formation, which comprises a visual identification assisted positioning algorithm and a formation flight control method. The visual identification assisted positioning algorithm comprises a preprocessing system, a target extraction system, a target scale estimation system and a visual continuous tracking correction system, the visual and LiDAR sensors can be utilized to realize assisted height measurement of an unmanned aerial vehicle cluster and positioning and crossing of a target obstacle, and the technical target of obstacle crossing in a complex outdoor environment is achieved. A continuous tracking and positioning algorithm based on monocular vision and a time domain filtering method based on a laser radar are adopted, and the positioning precision of the unmanned aerial vehicle crossing the obstacle target is improved.

The invention provides a category-level 6D attitude estimation method based on geometric perception key points, and relates to the technical field of attitude estimation.The method comprises the steps that on the basis that RGB images and point cloud features are fused, a dynamic key point proposing module is designed to generate key points which are consistent in category and geometrically adaptive, so that the adaptability to intra-class deformation and weak texture targets is enhanced; secondly, introducing a spatial geometric attention module to model a spatial structure relationship between key points so as to optimize key point distribution; and finally, point cloud reconstruction and scale regression are simultaneously realized through a geometric perception reconstruction and scale estimation module under the prior condition of no CAD model, and end-to-end optimization is carried out by utilizing multi-task loss. Experimental results on REAL275, CAMERA25 and House Cat6D data sets show that the method provided by the invention is superior to the existing method in multiple indexes such as IoU and attitude precision, and shows stronger generalization and robustness in a real complex scene.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify two-dimensional images via scene-based editing using three-dimensional representations of the two-dimensional images. For instance, in one or more embodiments, the disclosed systems utilize three-dimensional representations of two-dimensional images to generate and modify shadows in the two-dimensional images according to various shadow maps. Additionally, the disclosed systems utilize three-dimensional representations of two-dimensional images to modify humans in the two-dimensional images. The disclosed systems also utilize three-dimensional representations of two-dimensional images to provide scene scale estimation via scale fields of the two-dimensional images. In some embodiments, the disclosed systems utilizes three-dimensional representations of two-dimensional images to generate and visualize 3D planar surfaces for modifying objects in two-dimensional images. The disclosed systems further use three-dimensional representations of two-dimensional images to customize focal points for the two-dimensional images.

The invention relates to the technical field of engineering surveying and mapping, in particular to a structural point cloud principal axis identification method based on adaptive weighted principal component analysis, which comprises the following steps: acquiring a point cloud data set, initializing a weight set, and performing weighted centralization on the point cloud data set after point cloud downsampling; calculating a weighted covariance matrix according to the point cloud data set after weighted centralization, and obtaining a feature value and a normalized feature vector through the weighted covariance matrix; obtaining a distance measurement set of all point clouds; calculating an iteration weight set; and calculating the eigenvalue of the weighted covariance matrix and outputting an iterative spindle rotation angle. In the application of the method, along with the increase of the complexity of the point cloud, the advantage of adaptive weight estimation is more remarkable than that of common scale estimation, so that the high-precision rapid convergence performance when the common scale estimation is used for processing the uniform and symmetrical point cloud is reserved; and interference of outliers and environmental noise points on main shaft identification can be effectively prevented when highly non-uniform distribution point clouds are processed.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify two-dimensional images via scene-based editing using three-dimensional representations of the two-dimensional images. For instance, in one or more embodiments, the disclosed systems utilize three-dimensional representations of two-dimensional images to generate and modify shadows in the two-dimensional images according to various shadow maps. Additionally, the disclosed systems utilize three-dimensional representations of two-dimensional images to modify humans in the two-dimensional images. The disclosed systems also utilize three-dimensional representations of two-dimensional images to provide scene scale estimation via scale fields of the two-dimensional images. In some embodiments, the disclosed systems utilizes three-dimensional representations of two-dimensional images to generate and visualize 3D planar surfaces for modifying objects in two-dimensional images. The disclosed systems further use three-dimensional representations of two-dimensional images to customize focal points for the two-dimensional images.

An XR system is provided that enhances user interaction within extended reality environments through precise hand scale estimation. The XR system is configured to capture tracking data of a user's hand as the user interacts with a mobile device. Concurrently, the XR system captures pose data of itself and uses the tracking data and the pose data to determine a reference line segment. This segment aids in calculating three-dimensional distances between node pairs of the user's hand. By employing these measurements, the XR system effectively calculates a hand scale factor that is used for accurately integrating the user's hands into an XR user interface.

Examples in the present disclosure relate to temporally sparse scale estimation for object tracking. A computing device detects a current orientation of an object. The computing device determines that a difference between the current orientation and at least one of a plurality of previously detected orientations of the object is less than a threshold value. Each previously detected orientation has a respective scale estimate. In response to determining that the difference is less than the threshold value, the computing device generates an effective scale estimate for the object based on a combination of the respective scale estimates for the plurality of previously detected orientations. Each respective scale estimate contributes to the effective scale estimate according to a respective difference between the current orientation and the previously detected orientation for the respective scale estimate. The computing device tracks a pose of the object based on the effective scale estimate.

The invention provides an image gray scale estimation method and system, computer equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: projecting light stripes to a plane where a calibration plate is located, and calibrating a plane equation where each light stripe is located; projecting a plurality of pieces of structured light to the surface of the detected tubular object, collecting reflected light rays of the detected tubular object, and extracting light stripe centers; carrying out surface reflection coefficient relative value calibration on the plane of the measured tubular object to obtain a plurality of surface reflection coefficient values of the surface of the measured tubular object; and averaging the plurality of reflection coefficients, and calculating a theoretical gray value of the surface of the measured tubular object according to the average value of the plurality of reflection coefficients. According to the method, the gray level distribution of the surface of the object can be accurately estimated under the specific illumination condition through optical simulation, the influence of factors such as illumination and reflection on the gray level of the image is better simulated and optimized, more reliable data support is provided for defect detection of the tubular object, and the precision and robustness of structured light three-dimensional scanning are improved.

Examples in the present disclosure relate to scale estimation for facilitating extended reality (XR) experiences. An image of a hand of a user is captured via one or more optical sensors of an XR device. The image is processed to detect a hand pose relative to the XR device. A hand scale estimate corresponding to the detected hand pose is accessed. The hand scale estimate is one of a plurality of hand scale estimates each uniquely associated with a respective hand pose. The hand scale estimate is applied to generate positional data for one or more features of the hand of the user. The XR device tracks the hand of the user based on the positional data while the user uses the XR device.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify two-dimensional images via scene-based editing using three-dimensional representations of the two-dimensional images. For instance, in one or more embodiments, the disclosed systems utilize three-dimensional representations of two-dimensional images to generate and modify shadows in the two-dimensional images according to various shadow maps. Additionally, the disclosed systems utilize three-dimensional representations of two-dimensional images to modify humans in the two-dimensional images. The disclosed systems also utilize three-dimensional representations of two-dimensional images to provide scene scale estimation via scale fields of the two-dimensional images. In some embodiments, the disclosed systems utilizes three-dimensional representations of two-dimensional images to generate and visualize 3D planar surfaces for modifying objects in two-dimensional images. The disclosed systems further use three-dimensional representations of two-dimensional images to customize focal points for the two-dimensional images.

The invention relates to the technical field of ore body three-dimensional positioning prediction, and discloses a hidden ore body three-dimensional positioning prediction method based on artificial intelligence, and the method comprises the steps: firstly processing geological data through data preprocessing and feature extraction, and then carrying out the training and prediction through a constructed neural network model, and three-dimensional positioning of the concealed ore body is realized. The method comprises the steps of data normalization, missing value filling, feature extraction, neural network model construction, gradient descent updating, three-dimensional coordinate conversion, ore body scale estimation and the like. Through prediction precision evaluation and model optimization, the accuracy and efficiency of concealed ore body positioning are improved. The invention provides an efficient and accurate three-dimensional positioning prediction method for mineral resource exploration, and the method has a wide application prospect.

Systems, methods, and other embodiments described herein relate to using salient features to improve scale awareness in a depth model. In one embodiment, a method includes acquiring an image depicting surrounding objects present in an environment. The method includes selecting a salient object from the surrounding objects. The method includes determining characteristics of the salient object according to a language model. The method includes adapting a depth model according to the characteristics.

The invention provides a method and a device for constructing a three-dimensional simulation scene driven by multi-modal data, and relates to the technical field of computer vision. Comprising the following steps: acquiring and preprocessing multi-modal data so as to encode the multi-modal data into a joint embedded vector; generating a scene layout image according to the joint embedded vector and a condition image generation model, and segmenting the scene layout image according to a semantic segmentation expert model and a soft voting mechanism to determine a semantic segmentation result; performing boundary extraction on the semantic segmentation result to determine a standardized scene semantic graph; selecting an alternative digital asset set based on a category label corresponding to the semantic segmentation result and a simulation engine digital asset library; inputting the scene layout image into a scale estimation model to output a scene map scale; and generating a three-dimensional simulation scene according to the standardized scene semantic graph, the alternative digital asset set, the scene map scale and a two-dimensional layout algorithm. According to the invention, the scene synthesis effect from the multi-modal intention to the landing simulation scene is improved.

Machine Learning based scaling estimation in the art mostly used a complex guided wave setup. Further, selection of features for ML is critical to the predict significant points on the US signal response providing appropriate but minimum features that capture maximum scaling characteristics, thus keeping the ML features minimal to provide time and resource efficient computation. A method and system for estimation of scaling in objects by processing ultrasound responses using ML is disclosed. The system uses low-voltage pulse packets to generate ultrasonic (US) waves, capable of penetrating metal structures, using an economically repurposed piezoelectric transducer. These US signal response from the object-scaling interface and scaling-fluid interface, is captured using the repurposed piezoelectric transducer and processed to generate the envelope of the US signal response, and unique 42 features are extracted. A pretrained ML model processes the features to estimate various levels of scaling present in the object.

Methods, systems, and devices for wireless communications are described. The described techniques provide for a user equipment (UE) to perform receiver-side noise statistics (Rnn) estimation and for an extended reality (XR) device to perform log likelihood ratio (LLR) scaling estimation. For example, the UE may transmit a first set of reference signals to the XR device. The XR device may sample the reference signals and transmit an indication of quantized samples such that the UE may perform Rnn estimations and generate a pre-equalization matrix. The UE may transmit a set of pre-equalized reference signals to the XR device such that the XR device may estimate LLR scaling coefficients. In some examples, the UE may determine the pre-equalization matrix by performing a filtering of reference signal measurements. The UE may apply whitening to account for momentary interference, and may indicate a whitening matrix to the XR device.

The application provides a class-level 6D pose estimation method based on geometric perception key points, and relates to the technical field of pose estimation. On the basis of fusing RGB image and point cloud features, a dynamic key point proposal module is designed to generate class-consistent and geometric-adaptive key points to enhance the adaptability to intra-class deformation and weak texture targets. Secondly, a spatial geometry attention module is introduced to model the spatial structure relationship between key points to optimize the key point distribution. Finally, through the geometric perception reconstruction and scale estimation module, point cloud reconstruction and scale regression are simultaneously realized under the condition of no CAD model prior, and end-to-end optimization is carried out by using multi-task loss. The experimental results on REAL275, CAMERA25 and HouseCat6D datasets show that the method of the application is superior to existing methods in IoU and pose accuracy indexes, and has stronger generalization and robustness in real complex scenes.

The present application relates to the field of engineering surveying and mapping technology, and particularly relates to a structure point cloud principal axis identification method based on adaptive weighted principal component analysis, which comprises the following steps: obtaining a point cloud data set, initializing a weight set, and weighting and centralizing the point cloud data set after point cloud downsampling; calculating a weighted covariance matrix according to the weighted and centralized point cloud data set, and obtaining eigenvalues and normalized eigenvectors through the weighted covariance matrix; obtaining a distance measurement set of all point clouds; calculating an iterative weight set; and calculating the eigenvalues of the weighted covariance matrix to output an iterative principal axis rotation angle. In application, with the increase of point cloud complexity, the advantage of adaptive weight estimation is more and more significant compared with the common scale estimation, which not only retains the high-precision fast convergence ability of the common scale estimation when processing uniform and symmetric point clouds, but also effectively prevents the interference of outlier points and environmental noise points on the principal axis identification when processing highly non-uniformly distributed point clouds.

The present invention discloses a remote sensing image segmentation method that combines a quadtree structure with scale estimation. The method specifically includes remote sensing image preprocessing, quadtree partitioning, spatial segmentation scale estimation, attribute segmentation scale estimation, fractal network evolution algorithm segmentation, merging segmentation results, and accuracy verification. To address the image over-segmentation, under-segmentation, and mis-segmentation phenomena caused by the scale characteristics of different land types, the present invention uses the FNEA algorithm as a basis, a quadtree structure, and a scale estimation method based on spectral spatial statistics to achieve remote sensing image segmentation that includes multiple land feature features. This effectively improves segmentation accuracy and avoids the problems of linear land feature extraction interruption and classification errors caused by mis-segmentation and under-segmentation. This method is of great significance for remote sensing image classification and information extraction.

The invention provides an optimization and acceleration method and system for a visual inertial navigation system of an unmanned aerial vehicle based on heterogeneity. The method comprises the following steps: S1, outputting a reliable feature point set which is a direct input object for performing initial scale estimation in S2; and the feature points established in the step S1 become seed features tracked in the step S3. And S3, based on the position of the feature point determined in the S1, performing tracking by using a hardware acceleration optical flow in a subsequent frame. And the tracking result of the S3 enters the S4 for bidirectional optical flow consistency check, feature points which fail in tracking are eliminated, and a high-reliability feature trajectory is output. S4, taking the verified feature track as input of S5 feature management, and performing dynamic density control and quality evaluation. And S5, taking the managed feature distribution as a candidate set for feature selection in S6, and selecting an optimal subset for back-end optimization based on information gain evaluation in S6. S7, an adaptive mechanism adjusts the selection strategy in the step S6; and S8, overall system parameters are adjusted according to the state of the computing resource.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify two-dimensional images via scene-based editing using three-dimensional representations of the two-dimensional images. For instance, in one or more embodiments, the disclosed systems utilize three-dimensional representations of two-dimensional images to generate and modify shadows in the two-dimensional images according to various shadow maps. Additionally, the disclosed systems utilize three-dimensional representations of two-dimensional images to modify humans in the two-dimensional images. The disclosed systems also utilize three-dimensional representations of two-dimensional images to provide scene scale estimation via scale fields of the two-dimensional images. In some embodiments, the disclosed systems utilizes three-dimensional representations of two-dimensional images to generate and visualize 3D planar surfaces for modifying objects in two-dimensional images. The disclosed systems further use three-dimensional representations of two-dimensional images to customize focal points for the two-dimensional images.

The application belongs to the technical field of target tracking, and discloses a single-target long-time tracking method based on a deep neural network, which comprises the following steps: acquiring a tracking target image and a tracking search area image, building a feature extraction network based on a twin network, and performing feature extraction; in the tracking process, a target image is selected by a PNR score to be added to a template library and is sent to a model learning network to perform model learning and updating, a model target feature map and a search feature map are convolved to obtain a response map, the response map is sent to a scale regression network to obtain a scale position score map, and a target scale is acquired; when the target is occluded or lost, target recapture is performed; in the tracking process, information of the target and a suspected target is recorded, and when an interference object appears, a tracking position that is most matched with a historical track is selected. The application reduces model drift phenomenon, makes scale estimation more stable and accurate, and makes the algorithm suitable for long-time target tracking by using a re-search mechanism and an anti-interference matching technology.

Examples in the present disclosure relate to temporally sparse scale estimation for object tracking. A computing device detects a current orientation of an object. The computing device determines that a difference between the current orientation and at least one of a plurality of previously detected orientations of the object is less than a threshold value. Each previously detected orientation has a respective scale estimate. In response to determining that the difference is less than the threshold value, the computing device generates an effective scale estimate for the object based on a combination of the respective scale estimates for the plurality of previously detected orientations. Each respective scale estimate contributes to the effective scale estimate according to a respective difference between the current orientation and the previously detected orientation for the respective scale estimate. The computing device tracks a pose of the object based on the effective scale estimate.

The invention relates to a damper scale estimation method and system. The method comprises the following steps: acquiring an image of a damper to be identified; processing the image of the damper to be identified to obtain a damper cylinder region, a damper coarse scale line region and a damper fine scale line region; and performing scale identification on the coarse scale line area of the damper to obtain scale result data of the damper. According to the method, the damper cylinder region, the coarse scale line region and the fine scale line region in the damper image are identified, and scale identification is performed on the coarse scale line region to obtain the scale result data, so that the efficiency and the accuracy of damper scale identification are remarkably improved, and the accuracy of damper scale identification is improved. The problems that a traditional manual visual detection or photo recognition mode is tedious, the workload is large, and personal errors are likely to be introduced are solved.

The application provides a visual positioning scale compensation Kalman filtering method, which realizes online real-time estimation and dynamic compensation of scale error by fusing height information, kinematic constraint and visual observation through explicitly taking scale error factor as a state variable in a state space model of extended Kalman filtering (EKF). The technical scheme of the application is applied to solve the technical problems of unstable scale estimation and large accumulated error of pose in the prior art visual inertial odometer (VIO) system when facing severe motion, visual degradation or low-cost IMU zero bias.

The application discloses a geometric consistency and instrument anchoring three-dimensional reconstruction and polyp measurement method of an endoscope. The method first projects monocular image features to a Riemannian manifold space through a geometric consistency perception module, generates a high-fidelity relative depth map by using a manifold-constrained latent diffusion model, and maintains cross-domain structural consistency. Secondly, by using a scale estimation module based on instrument anchoring, biopsy forceps in a field of view are automatically identified and key points are extracted, a PnP solver and a Bayesian regression are used to solve six degrees of freedom of the instrument, and finally, geometric constraint equations are constructed based on the known physical size of the biopsy forceps, a global absolute scale factor is solved, and the relative depth map is converted into a metric three-dimensional point cloud. A few-sample linear calibration mechanism is introduced, and linear deviation caused by domain offset can be eliminated by using a small amount of clinical frames. According to the application, sub-millimeter polyp size measurement can be realized under monocular endoscopy by using conventional surgical instruments without additional hardware.

Embodiments include methods for synchronizing an augmented reality (AR) object placed in a 3D mesh onto a video feed. A computing device may first receive a video feed including a sequence of frames of a scene and depth or motion data captured by a camera. The computing device may generate a three-dimensional (3D) mesh based on the depth or motion data. The computing device may texture the 3D mesh to create a 3D model. Upon performing object recognition, the computing device may identify anchor points in the 3D model and anchor points in the video feed. The anchor points are used to calculate the location of the AR object.

The present invention relates to a complex scene single target tracking method, device, system and storage medium based on Staple algorithm. The tracking method of the present invention comprises: reading a new frame image, judging whether the current frame is a starting frame, if so, selecting the tracking target and returning to the step, if not, proceeding to the next step; calculating the HOG feature response and foreground probability of the previous frame; performing feature adaptive fusion; performing similar target re-identification; using a loss judgment mechanism module to judge whether the target is lost; performing scale estimation on the detected target; using a template update strategy to update three models; if the current frame is not the last frame, returning to the first step until the current frame is the last frame image. The present invention integrates and improves the background weight histogram, similar target re-identification, loss judgment, feature adaptive fusion and template update, which not only improves the accuracy of the algorithm, but also significantly enhances its adaptability and robustness in complex scenes.