27 results about "Relative depth" patented technology

The invention, which relates to the technical field of image processing, discloses a method for converting a 2D image into a 3D image, comprising the following steps: step 1, obtaining a 2D image; step 2, monocular depth estimation is carried out on the 2D image to obtain a relative depth map; 3, determining the longitudinal depth and the zero parallax surface of the scene according to the content of the 2D image; 4, carrying out value domain transformation on the depth map; 5, calculating the parallax of each pixel of the 2D image according to the transformed depth map, and then rendering an image of a virtual viewpoint according to the parallax; and step 6, repairing the cavity. According to the method for converting the 2D image into the 3D image, the watching experience is improved, the stereoscopic impression is enhanced, the ghosting phenomenon is reduced, and the proper proportion scale is kept; the conversion efficiency and the automation degree are improved, and manual intervention is reduced; meanwhile, the generalization ability is enhanced; in addition, the hole repairing effect is optimized, so that the converted 3D image is more natural and perfect.

A method for estimating a three-dimensional pose from control points of an image of the object, includes: selecting, for each pair consisting of first and second control points that define a skeleton of the object, relative positions of the first control point with respect to the second control point on the image, estimating, for each of the selected relative positions, a relative depth of the first control point with respect to the second control point over the entire image based on an assumption that the second control point exists at each position on the image; detecting two-dimensional positions of the control points on the image for the object using the image; obtaining, based on the relative depth estimated for each of the selected relative positions and the two-dimensional positions of the control points, relative three-dimensional positions of the control points; and estimating the three-dimensional pose for the object.

The invention discloses a transparent object depth completion method and system based on relative depth and mask attention, and belongs to the field of robots / artificial intelligence. The method comprises the following steps of: 1, acquiring multi-modal scene data and preprocessing the multi-modal scene data; 2, constructing a feature coding network based on mask attention and relative depth; 3, performing multi-feature fusion and full-image depth reconstruction on the feature coding network constructed in the step 2; and step 4, performing network training based on global loss optimization based on the multi-modal scene data preprocessed in the step 1 and reconstruction of the feature coding network in the step 3. The objective of the invention is to overcome the problem that the existing transparent object depth completion method is poor in accuracy and generalization in a real scene.

The invention relates to a gold thread height detection method, system and device for an optical module and a medium, and the method comprises the steps: carrying out the multi-scale convolution feature extraction of an image of the optical module, and fusing the V-channel features of the image into the convolution features; inputting the convolution feature map into a corresponding multi-scale detection head for gold thread positioning detection to obtain two-dimensional coordinate information of each gold thread; on the basis of the two-dimensional coordinate information, intercepting an interested area corresponding to each gold thread in the optical module image, and performing pixel-level depth detection on the interested area to obtain a relative depth map and a reference depth value of each gold thread; and performing abnormal value elimination on the relative depth map according to the Pauta criterion, and based on the obtained optimal relative depth map, combining the two-dimensional coordinate information and the corresponding reference depth value to obtain the actual height of the gold thread relative to the substrate. According to the invention, the gold thread height detection precision is improved, and especially the gold threads which are densely arranged, staggered and overlapped and are thin and long in shape have a relatively high detection effect.

The present application discloses an image processing method, apparatus, storage medium, and terminal, relating to the technical field of image processing. Determine the two-dimensional position information of each object key point in the target object in the two-dimensional coordinate system of the image and the relative depth relative to the reference point in the target object; according to the two-dimensional position information and the relative depth, determine the first three-dimensional position information of each object key point in the target object in the world three-dimensional coordinate system; and then determine the second three-dimensional position information of each model key point in the target model corresponding to the target object. Since the first three-dimensional position information of each object key point in the target object is first determined, and then the second three-dimensional position information of each model key point in the target model corresponding to the target object is determined according to the two-dimensional position information and the first three-dimensional position information of each object key point in the target object, it is possible to determine the three-dimensional position information of the target model corresponding to the target object through one image, reducing the complexity of determining the three-dimensional position.

The invention discloses a deblocking method based on explicit three-dimensional Gaussian scene layering, and the method comprises the steps: carrying out the constrained full-scene reconstruction through depth prior provided by a depth estimation base model, so as to obtain a three-dimensional Gaussian scene which meets the geometric requirements and is divided by a blocking object and a background; estimating the separation depth by utilizing the depth inconsistency between the two visual angles caused by the shielding object; learning and optimizing the separation depth of each view angle by using an adaptive weight learning method; and obtaining a non-shielding result by using the Gaussian of the separated depth splashing background. According to the invention, the depth estimation base model is used to provide the relative depth map with stronger geometric priori, and after the relative depth map is aligned with the actual scene depth, a scene with better geometry can be obtained through optimization; in the separation depth estimation part, the invention provides a method for determining the position and the separation depth of a shielding object by utilizing the inconsistency of the depths of different visual angles, and the optimization speed is greatly improved while various types of shielding are effectively removed.

The invention relates to the technical field of computer vision application, in particular to a material height measuring method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an environment image in a cabin through a preset camera, the environment image comprising stacked materials; performing depth estimation on the environment image by using a preset monocular depth model to generate a relative depth image, and converting the relative depth image into an absolute depth image based on the internal reference of the camera by taking the actual size of the hatch and the pixel size of the hatch in the environment image as a reference; and calculating the height of the material based on the absolute depth image. According to the measurement method, only a monocular camera is needed, the installation process is simple, the system is easy to maintain, and compared with a multi-sensor fusion scheme, the method is lower in installation and maintenance cost.

The present invention discloses a depth completion method based on images and extremely sparse three-dimensional points. The present invention uses deep learning and traditional optimization methods, respectively using different attributes of the input three-dimensional points to obtain correct depth estimates when the number of input sparse points is small. The present invention utilizes a set of depth basis to decouple the estimation of relative depth and absolute depth. The method is improved in terms of the robustness to the noise of the input three-dimensional points, and relatively good depth can be obtained in a system with high noise, so as to be applied to applications such as virtual reality and augmented reality.

This application discloses a 3D reconstruction method, storage medium, and computer program product, relating to the field of computer vision technology. The method includes: acquiring a sequence of unlabeled multi-frame video images; calling a feedforward 3D reconstruction model; obtaining ordinal depth loss, local affine invariant loss, and multi-view consistency constraint loss; the ordinal depth loss constrains the relative depth ordering of pixel pairs, the local affine invariant loss preserves locally aligned surface details, and the multi-view consistency constraint loss is used to determine the camera position and pose; jointly updating the model parameters using the ordinal depth loss, local affine invariant loss, and multi-view consistency constraint loss to obtain a 3D reconstruction optimization model; and inputting the multi-frame video image sequence into the 3D reconstruction optimization model for forward propagation to obtain 3D reconstruction information. By jointly updating the model parameters using three different loss functions, the reconstruction accuracy of dynamic scenes is improved without relying on expensive annotations and while preserving inference efficiency.

The application discloses a three-dimensional hand posture estimation method based on monocular RGB images, designs a feature enhancement method which can explicitly introduce the inherent skeleton structure of the hand, and adaptively enhances the features of the joint nodes to be estimated by using the associated information, so that the accuracy of hand posture estimation is finally improved. The designed method is as follows: firstly, convolutional neural network is used to extract joint node level semantic features and skeleton level semantic features from the input hand image, and a feature fusion module is used to cross semantic aggregation of the two features; then, the feature adaptive enhancement module can adaptively enhance the related features of each joint node by using the associated information thereof; then, the joint node two-dimensional heat map and the relative depth map are obtained through the output layer, and a multi-stage optimization strategy is adopted to continuously refine the two-dimensional heat map and the depth map to estimate more accurate hand two-dimensional joint node coordinates and relative depth; finally, the final hand three-dimensional coordinate information is calculated by using the camera parameters.

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vison, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vison, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

The present invention discloses a polarization three-dimensional imaging method capable of characterizing the absolute depth of a target, comprising: obtaining polarization images of the target at different polarization angles; obtaining the degree of polarization at each pixel point on the surface of the target object in the polarization image; obtaining the initial values of the azimuth angle and the initial values of the incident angle at different pixel points on the target surface; correcting the initial values of the azimuth angle and the incident angle; constructing a normal vector for each pixel point on the target surface using the corrected azimuth angle and the incident angle; reconstructing the three-dimensional contour of the target using the normal vector integral to obtain the surface function of the target; and combining the camera calibration and the surface function to obtain the absolute depth information of the target. The present invention recharacterizes the target surface normal vector using the component of the target surface normal vector along the depth direction as a unit. The three-dimensional model reconstructed based on the surface normal vector has a definite changing relationship with the absolute depth information of the target, breaking the limitation of traditional polarization three-dimensional imaging that displays depth as relative depth.

The application relates to a monocular camera absolute depth acquisition method, device, equipment and storage medium. The method comprises the following steps: acquiring a training image containing two ground features photographed by a monocular camera and the absolute distance between the two ground features in the training image; identifying the training image to acquire the pixel coordinates and the relative depth of the two ground features in the training image; obtaining the relative distance between the two ground features in the training image according to the pixel coordinates, the relative depth and the camera internal parameter matrix of the monocular camera; and obtaining the absolute depth of the two ground features in the training image according to the ratio of the absolute distance to the relative distance between the two ground features in the training image and the relative depth. The scheme provided by the application can reduce the influence of scene transformation on the absolute depth acquisition process and obtain accurate absolute depth information.

The invention discloses a hand reconstruction model training method, a hand reconstruction method and a hand reconstruction device, which are applied to the field of three-dimensional reconstruction, and comprise the following steps: carrying out feature extraction on hand pictures in a training data set to obtain global features; extracting the global features to respectively obtain left and right hand positioning features, left and right hand joint region segmentation features and left and right hand interaction features; performing feature fusion on the left and right hand positioning features, the left and right hand joint region segmentation features and the left and right hand interaction features to obtain fusion features; based on the fusion features, performing regression on the left-right hand MANO coefficient and the camera parameters, and performing hand reconstruction by using the regressed left-right hand MANO coefficient and the regressed camera parameters to obtain a reconstructed hand; and training the hand reconstruction model according to the reconstructed hand and the real hand to obtain a trained hand reconstruction model. According to the method, hand reconstruction of any scene can be carried out based on a single picture, and the reconstructed hand posture and shape and the relative depth position between the two hands are accurate.

The invention discloses a three-dimensional reconstruction method and system based on distillation sampling and context matching, and relates to the technical field of three-dimensional reconstruction and distillation learning. According to the method, firstly, the influence of equipment difference and individual difference on data quality is effectively eliminated through screening of the OCT image containing the cup and disc structure, center cutting, normalization processing and mask generation; besides, a generative model combining a noise scheduler and a UNet network is introduced, relative depth calculation is introduced by using internal and external parameters of a camera, and the model can accurately learn medical image feature distribution through a progressive noise adding and denoising mechanism. According to the method, the consistency between the two-dimensional images is measured by using a distillation sampling method, and more images are generated based on a context matching thought and are used for further calibrating the context relationship, so that the relationship between the two-dimensional images is processed more accurately. In this way, the problem of artifacts among the reconstructed three-dimensional images can be solved, and the fine degree of the three-dimensional images can be improved to a certain extent.

The invention provides an absolute depth estimation method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: performing depth estimation processing on an image shot by a camera through a depth estimation network to obtain relative depths respectively corresponding to a plurality of pixel points in the image; projecting the plurality of pixel points to a three-dimensional space according to the relative depths respectively corresponding to the plurality of pixel points; according to the included angle between the surface normal vector of each pixel point in the three-dimensional space and an ideal ground normal vector, screening the plurality of pixel points to obtain ground points; calculating the estimated height of the camera according to the ground point, and calculating a scale factor according to the estimated height and the actual height of the camera; and according to the scale factor, carrying out scaling processing on the relative depths corresponding to the plurality of pixel points respectively to obtain absolute depths corresponding to the plurality of pixel points respectively. According to the invention, the absolute depth can be accurately estimated without increasing extra cost.

An optical detector determines a relative depth between a first position and a second position on an inclined surface. A controller determines a slope of the inclined surface based on the relative depth between the first position and the second position and the distance moved between positions. An actuator displaces a punch at a plurality of intermediate positions on the inclined surface located between the first position and the second position. A displacement detector determines a depth at which the sample is touched by the punch at each of the plurality of intermediate positions. The controller further determines a calculated depth of the inclined surface at each of the plurality of intermediate positions based on the slope of the inclined surface and the distance moved between positions. The controller further compares the depth measured by the displacement detector to each corresponding calculated depth to verify accuracy of the displacement detector.

A method for estimating depth implemented in an electronic device includes obtaining a first image; inputting the first image into a depth estimation model, and obtaining a first depth image; obtaining a depth ratio factor, the depth ratio factor indicating a relationship between a relative depth and a depth of each pixel in the first depth image; and obtaining depth information of the first depth image according to the first depth image and the depth ratio factor.

The invention relates to the technical field of electric vehicles, and discloses a data processing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed target two-dimensional image; according to the target two-dimensional image, determining a two-dimensional detection frame of an observation object in the target two-dimensional image and a first relative depth of the observation object; determining a plurality of second relative depths of the observation object under different dimensions according to the two-dimensional detection frame; determining the relative depth of the observation object according to the first relative depth and the plurality of second relative depths; and generating three-dimensional motion information corresponding to the observation object according to the relative depth and the two-dimensional detection frame. According to the invention, based on pure vision object detection, the relative depth of the object can be accurately determined by integrating a plurality of depths, the implementation mode is simple, the required calculation amount is small, and excessive calculation power does not need to be consumed.

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vison, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

The invention discloses an object detection method and device, and the method comprises the steps: carrying out the depth calculation of a first image, obtaining a first relative depth image, and enabling a camera to shoot a scene containing at least one target object; converting the first relative depth image into a second relative depth image according to the scale correction parameter associated with the camera; and determining position information of an obstacle in at least one target object according to the second relative depth image. According to the method, scale conversion can be performed on the first relative depth image provided by the general model by using the scale correction parameter associated with the camera to obtain the second relative depth image, so that obstacle detection can be performed by using the point cloud data corresponding to the second relative depth image, and the degree of influence of internal and external parameters of the camera on a detection result can be reduced; the accuracy and universality of detecting the object in the image can be improved.

The invention discloses an interaction method, device and equipment based on intention perception and adaptive depth partitioning, and a computer readable storage medium. The interaction method comprises the following steps: acquiring a user shoulder key point coordinate to determine an original point and a reference scale of an interaction coordinate system; determining an entry threshold value and an exit threshold value of the Z axis based on the reference scale, wherein the entry threshold value is greater than the exit threshold value; obtaining the key point coordinates of the wrist of the user to calculate the Z-axis relative depth, the Z-axis speed and the X-Y plane speed of the wrist; switching a current interaction mode between a first interaction mode and a second interaction mode based on the depth, the speed and the threshold value; and according to the current interaction mode, generating a two-dimensional movement interaction instruction based on the X-Y plane displacement of the wrist, or generating a one-dimensional continuous parameter interaction instruction based on the Z-axis relative depth. The method has the advantages that mode flicker and mistaken touch are avoided, the interaction dimension is expanded, and the interaction stability and richness are improved.

Embodiments of the present application provide a depth estimation method and device, electronic equipment and storage medium, which are applied to electronic equipment, the electronic equipment comprising a camera device for shooting, the method comprising: obtaining a first image; inputting the first image into a depth estimation model to obtain a first depth image; obtaining a depth scale factor, wherein the depth scale factor is used to indicate a relationship between a relative depth of a pixel point on the first depth image and a depth value of the pixel point; and calculating depth information of the first depth image according to the depth scale factor and the first depth image. The actual depth value can be obtained without knowing the distance between left and right cameras of a binocular stereo camera in advance, and the actual depth value can be accurately known when using a monocular camera.

An optical detector determines a relative depth between a first position and a second position on an inclined surface. A controller determines a slope of the inclined surface based on the relative depth between the first position and the second position and the distance moved between positions. An actuator displaces a punch at a plurality of intermediate positions on the inclined surface located between the first position and the second position. A displacement detector determines a depth at which the sample is touched by the punch at each of the plurality of intermediate positions. The controller further determines a calculated depth of the inclined surface at each of the plurality of intermediate positions based on the slope of the inclined surface and the distance moved between positions. The controller further compares the depth measured by the displacement detector to each corresponding calculated depth to verify accuracy of the displacement detector.

The present application relates to a method, device, electronic device, and storage medium for obtaining the absolute depth of a monocular camera. The method includes: obtaining the current conversion coefficient and the current absolute depth information for converting the relative depth information into the absolute depth information based on the relative depth information, pixel coordinates, and the camera intrinsic parameter matrix of the monocular camera of two target features, and obtaining the three-dimensional coordinates of each pixel point of the two target features; obtaining the next conversion coefficient and the undetermined absolute depth information for converting the relative depth information into the absolute depth information based on the pixel coordinates, relative depth information, and the camera intrinsic parameter matrix corresponding to the three-dimensional coordinates of each pixel point of the two target features; if the difference between the next conversion coefficient and the current conversion coefficient is less than a set coefficient threshold, then determining the undetermined absolute depth information of the two target features as the absolute depth information of the two target features. The solution provided by the present application can accurately obtain the absolute depth of an object in an image captured by a monocular camera.