98 results about "3d geometry" patented technology

The invention discloses a three-dimensional reconstruction method based on a pulse camera and electronic equipment, and relates to the technical field of three-dimensional reconstruction. According to the method, the problems of insufficient spatial-temporal information modeling and inaccurate initial attitude estimation in pulse camera three-dimensional reconstruction in related technologies are effectively solved, the precision of a three-dimensional reconstruction model is improved, a three-dimensional scene with higher quality and more realistic sense can be generated, meanwhile, efficient alignment of pulse data and three-dimensional geometry is realized through joint optimization of a framework, and the accuracy of the three-dimensional reconstruction model is improved. And the overall robustness and generalization ability of the system are enhanced.

A method of implementing directional opacity handling in Gaussian splats is disclosed. An approximate representation of a scene is received. 3D geometry data is generated. The 3D geometry data includes an approximate position and normal of each splat in a set of splats in the scene. Directional opacity values are calculated for the set of splats based on an application of a machine-learning model trained to optimize values of splat parameters, the values including the directional opacity values. The calculated directional opacity values are applied to the set of splats to enhance processing of the set of splats.

A method for generating a view of an event in a video game is provided, including the following operations: capturing two-dimensional (2D) gameplay video generated from a session of a video game; analyzing the 2D gameplay video to identify an event occurring in a scene depicted in the 2D gameplay video and identifying one or more elements involved in said event; further analyzing the 2D gameplay video to determine 3D geometry of the scene; using the 3D geometry of the scene to generate a 3D video asset of the event that occurred in the gameplay video; generating a 2D view of the 3D video asset for presentation on a display, wherein generating said 2D view includes determining a field of view (FOV) to apply for the 2D view, the FOV being configured to include the elements involved in the event.

An embodiment includes a method of determining a collision-free space for a robotic welding system. The method includes fixing a location of a part to be welded in a 3D coordinate space of a robotic welding system. An arm of the robotic welding system is moved around the part within the 3D coordinate space. Data corresponding to positions and orientations of the arm in the 3D coordinate space are recorded as the arm is moved within the 3D coordinate space around the part. The data is translated to swept volumes of data within the 3D coordinate space. The swept volumes of data are merged to generate 3D geometry data representing a continuous collision-free space within the 3D coordinate space.

An automated cage-to-cage fitting technique is used to fit arbitrary three-dimensional (3D) geometry to arbitrary target 3D geometry for 3D avatars in a 3D environment, such as an online game. Each graphical representation of items of clothing is provided with an inner cage and with an outer cage. The inner cage of a clothing item (a current layer) is mapped to the outer cage of another clothing item (a previous / underlying layer) that has already been fitted on the 3D avatar. This mapping enables the current layer to conform to the shape of the previous layer. The technique allows any body geometry to be fitted with any clothing geometry, including enabling layers of clothing to be fitted over underlying layer(s) of clothing, thereby providing customization.

The present invention relates to the technical field of buildings, and in particular, to a visual inspection and measurement method for a reinforcing mesh, comprising the steps of: using an RGBD camera to perform data acquisition on a building site; performing Mask R-CNN instance segmentation on RGB, preliminarily identifying a workpiece, and segmenting a 2D workpiece from the background; converting the obtained 2D workpiece into a 3D point cloud on the basis of an intrinsic mapping relationship of the RGBD camera; and for on-site acquired data, obtaining a segmentation result of rebar details by means of FPFH feature matching with an offline model; and on the basis of an identified rebar point cloud, performing cylinder fitting to obtain the number of rebars within the field of view. According to the present invention, preliminary identification of rebars is performed on a 2D site image by means of Mask R-CNN instance segmentation, and then rebar 3D geometry is precisely identified by using FPFH feature consistency.

A technique for generating a sequence of geometries includes converting, via an encoder neural network, one or more input geometries corresponding to one or more frames within an animation into one or more latent vectors. The technique also includes generating the sequence of geometries corresponding to a sequence of frames within the animation based on the one or more latent vectors. The technique further includes causing output related to the animation to be generated based on the sequence of geometries.

A method for generating a physical object is provided, including: capturing two-dimensional (2D) gameplay video generated from a session of a video game; analyzing the 2D gameplay video to identify a virtual object depicted in the 2D gameplay video; further analyzing the 2D gameplay video to determine 3D geometry of the virtual object; using the 3D geometry of the object to generate a 3D model of the virtual object; storing the 3D model to a user account; using the 3D model to generate a physical object resembling the virtual object.

Unsupervised volumetric 3D animation (UVA) of non-rigid deformable objects without annotation learns the 3D structure and dynamics of the object only from a single view red / green / blue (RGB) video and decomposes the single view RGB video into semantically meaningful portions that can be tracked and animated. Using a 3D automatic decoder framework, the UVA model learns 3D geometry and partial decomposition of underlying objects from still or video images in a fully unsupervised manner via a microperspective n-point (PnP) algorithm pairing with a keypoint estimator. This allows the UVA model to perform 3D segmentation, 3D keypoint estimation, novel view synthesis, and animation. The UVA model may obtain animatable 3D objects from a single or several images. The UVA method also characterizes a space in which all objects are represented in their normative, animated ready form. Applications include creating a shot from an image or video of a social media application.

A method for generating a three-dimensional (3D) content moment from a video game is provided, including the following operations: capturing two-dimensional (2D) gameplay video generated from a session of a video game; analyzing the 2D gameplay video to determine 3D geometry of a scene depicted in the 2D gameplay video; using the 3D geometry of the scene to generate a 3D video asset of a moment that occurred in the gameplay video; storing the 3D video asset to a user account.

Optical sensors (e.g., cameras) and (e.g., IR) illumination sources may be distributed in an environment (e.g., an interior space such as a cabin or cockpit of an ego-machine) and synchronized to generate frames of sensor data, which may be used to reconstruct 3D geometry and / or 3D pose of an occupant, operator, or other object in the environment. For example, stereo vision may be used to generate one or more depth maps from image data generated using different cameras, the depth map(s) may be transformed into a 3D point cloud, and surface reconstruction may be applied to reconstruct the 3D geometry of surface(s) in the environment. A 3D pose, one or more keypoints (e.g., facial landmarks), or some other representation of the shape of the reconstructed surface(s) may be extracted from the reconstructed surface and used in one or more downstream tasks, such as driver and / or occupant monitoring tasks.

One embodiment of a method for generating an articulation model includes receiving a first set of images of an object in a first articulation and a second set of images of the object in a second articulation, performing one or more operations to generate first three-dimensional (3D) geometry based on the first set of images, performing one or more operations to generate second 3D geometry based on the second set of images, and performing one or more operations to generate an articulation model of the object based on the first 3D geometry and the second 3D geometry.

One embodiment of the present invention sets forth a technique for training a machine learning model on a geometry generation task. The technique includes generating, via execution of a diffusion model, a first set of training output corresponding to a first set of three-dimensional (3D) geometries based on a first set of conditioning inputs associated with a first conditioning mode, and training the diffusion model based on a first set of loss values associated with the first set of training output. The technique further includes generating, via execution of the diffusion model and a first adapter model, a second set of training output corresponding to a second set of 3D geometries based on a second set of conditioning inputs associated with a second conditioning mode, and training the first adapter model based on a second set of loss values associated with the second set of training output.

The invention discloses a visual angle controllable continuous image generation system and method based on three-dimensional point clouds, and relates to the technical field of crossing of computer vision and graphics, the visual angle controllable continuous image generation system comprises a 3D geometric coding module, a condition rendering module and a video generation module, the 3D geometric coding module is used for constructing and updating dynamic 3D scene representation in real time, and the condition rendering module is used for rendering the dynamic 3D scene representation in real time; the condition rendering module is used for generating 2D rendering frames with geometric constraints, the video generation module completes video synthesis based on a diffusion model, and for continuous frame requirements, feature matching information is injected and cache is updated in a blocking manner by combining a ControlNet subnet. By means of a bidirectional projection mechanism, on the basis that 3D point cloud is rendered into 2D frames with holes through forward projection, 3D cache of the complemented RGB-D frames is reversely updated through reverse projection, dynamic balance of geometric consistency and content updating is ingeniously achieved, the content can be updated in time while the geometric structure of the generated image is kept accurate, and the image quality is improved. And the authenticity and the continuity of the image are enhanced.

In various examples, optical depth estimation for interior space monitoring systems and applications is disclosed. Absolute 3D depth estimates from monocular image data may be generated using a machine learning model using 3D geometry priors and a joint learning framework that combines depth estimation with object segmentation. Three-dimensional geometry priors provide surface-level information that enriches the model's understanding of the relevant spatial geometry and resolves scale ambiguity in monocular depth estimation within automotive in-cabin environments. The model may include a common (e.g., shared) encoder stage that outputs features extracted from an optical image sensor feed to separate decoder stages that include a depth estimation decoder and a segmentation decoder. Joint learning for depth estimation and segmentation tasks during training achieves a more nuanced understanding of the in-cabin environment, leading to significantly improved depth accuracy.

Optical sensors (e.g., cameras) and (e.g., IR) illumination sources may be distributed in an environment (e.g., an interior space such as a cabin or cockpit of an ego-machine) and synchronized to generate frames of sensor data, which may be used to reconstruct 3D geometry and / or 3D pose of an occupant, operator, or other object in the environment. For example, stereo vision may be used to generate one or more depth maps from image data generated using different cameras, the depth map(s) may be transformed into a 3D point cloud, and surface reconstruction may be applied to reconstruct the 3D geometry of surface(s) in the environment. A 3D pose, one or more keypoints (e.g., facial landmarks), or some other representation of the shape of the reconstructed surface(s) may be extracted from the reconstructed surface and used in one or more downstream tasks, such as driver and / or occupant monitoring tasks.

The invention discloses an image adjustment 3D content creation architecture based on 2D diffusion, and relates to the technical field of diffusion models, and the architecture comprises a diffusion model which is composed of a plurality of cross attention blocks in a U-Net structure and supports effective fusion of various modes of texts, images and camera parameters. In the present invention, it is devoted to create 3D content using a potential diffusion model. The 3D geometry is not generated directly through a potential diffusion model, but a two-stage formula is employed. First, a potential diffusion model of view conditioned reflex is organized, and a multi-view image is synthesized with a monocular image and the outside of a camera as inputs. Next, a neural radiation field is trained using the synthesized multi-view image, which is easily optimized as volume rendering is differentiable. After the neural radiation field training is completed, a 3D geometric model is generated through an advancing cube algorithm applied to a density field. The framework eliminates the requirement for pairing 3D training data and does not require a large amount of computing resources.

An electronic apparatus for training an AI (Artificial Intelligence) model to generate an image of an object based on an input pose comprises at least one processor including processing circuitry and memory storing instructions. The instructions, when executed by the at least one processor individually or collectively, cause the electronic apparatus to obtain a plurality of images for training the AI model, obtain training images including an object from the plurality of images, obtain at least one parameter corresponding to each of the training images, obtain 3D geometry information of the object based on the at least one parameter, obtain a spherical neural texture mapped with the 3D geometry information, obtain warping prediction information for the spherical neural texture, update the spherical neural texture based on the warping prediction information, and train the AI model based on the updated spherical neural texture.

A method for generating a three-dimensional (3D) content moment from a video game is provided, including the following operations: capturing two-dimensional (2D) gameplay video generated from a session of a video game; analyzing the 2D gameplay video to determine 3D geometry of a scene depicted in the 2D gameplay video; using the 3D geometry of the scene to generate a 3D video asset of a moment that occurred in the gameplay video; storing the 3D video asset to a user account.

A method, system, and apparatus for computer-aided design and manufacture of a physical structure using hybrid additive and subtractive manufacturing, including media-encoded computer program products, includes, in one aspect, a method comprising: obtaining data on the 3D geometry of a part; simulating at least a portion of a manufacturing process, said at least a portion including adding a first material in a first stage and removing a second material in a second subsequent stage, wherein the second material comprises a portion of the first material, removing the second material includes mixing between the materials added in the first and second stages, and simulating the thermal effects of adding and removing materials in the first and second stages; and adjusting the amount of said portion based on the simulation results to prevent the part from deviating from its three-dimensional geometry, said deviation resulting in insufficient material available for mixing.

The present invention provides improved systems and methods for 3D geometry evaluation using vertex distance from center and mesh triangle vertex distance hashing. The present invention allows the generation of a unique hash that is immune to scale, position, orientation, and being inverted along any axis. This allows for the ability to store a single unique hash for each reference geometry that is calculated in advance, generate the unique hash for a search geometry, and then search for matching hash values. The actual geometries do not have to be stored, and only one string value, the hash value, is being searched.

A computer-implemented method for marking an object (5) on a 3D image, comprising the steps of: providing a 3D image comprising an object (5); displaying at least one 2D slice (10) of the 3D image to a user and locating a 3D geometry within the 3D image; receiving location information of the 3D geometry selected by the user; automatically defining a 3D region of interest (7) within the 3D image comprising the 3D geometry and cropping image data from the 3D region of interest (7) from the 3D image; a trained algorithm is applied to the image data from the 3D region of interest (7), where the output of the trained algorithm is a 3D volume (9) within the 3D region of interest (7) predicted to cover the object (5).

Generating a rendered image comprises: generating 904 a segmentation mask for an input 3D geometry; generating a plurality of texture maps associated with portions of the geometry; generating 906 a pl

In an example method, an NWM molding blank comprising a nonwoven material held compressed by an adhesive is placed in a separable mold and heated to a melting temperature of the adhesive. The molding blank expands, shaping into an NWM molded object having a 3D geometry as an intermediate NWM object. The intermediate NWM object is cooled through a temperature band having an upper boundary and a lower boundary, and further cooled to a solidification temperature of the adhesive. The upper boundary is above the solidification temperature and the lower boundary is below the adhesive melting temperature. While in the temperature band, the mold is separated so that exposed surfaces of the intermediate NWM object are accessible. The object is then transported to a contoured forming surface of a forming bed by an actuatable arm having an end effector that grips the exposed surfaces via vacuum suction, the object is lifted from the mold and placed on the contoured forming surface. Optionally, the end effector contacting surfaces include final forming features. The actuatable arm compresses the intermediate NWM object against the contoured forming surface and, optionally, the end effector final forming features, and continues compression until cooled to the solidification temperature.

Embodiments provide camera designs (e.g., eyeball cameras) that mimic the human eye in terms of geometry, optical properties, and / or motion. The eyeball camera uses the same cornea and pupil geometry as the human eye, and has an iris and pupil configured with a variety of textures, colors, or diameter options. The resolution of the eyeball camera is designed to match the acuity of typical 20 / 20 human vision, and the focal length is adjusted from 0 to 4 diopters. A pair of eyeball cameras are independently mounted on two hexapods to simulate the gaze and convergence of the human eye. The virtual and real worlds perceived are calibrated and evaluated based on eye conditions such as pupil position and gaze using eyeball cameras. The eyeball camera acts as a bridge to combine data from spatial computing (such as eye tracking, 3D geometry of the digital world, display color accuracy / uniformity, and display optical quality (clarity, contrast, etc.)).

The present invention concerns a system and a method for collision avoidance of a 3D robotic concrete printer, whereby the method comprises the steps of performing an application and / or manipulation process by executing instructions, such as based on G-Code, with a 3D robotic concrete printer by moving a tool in a path for applying and / or manipulating concrete material; and moving the tool in response to commands to a motion planner, said movement being assisted by a model-based collision prediction system, whereby the model-based collision-prediction system involves the steps of inferring and generating 3D geometries of concrete structures from applying and / or manipulating concrete structures by commanding the 3D robotic concrete printer, using the 3D geometries as a 3D collision model, which allow at any point in time to check if parts of the 3D robotic concrete printer would yield a collision when performing said movement; whereby the 3D collision model of the concrete structure is developed in parallel to the application and / or manipulation process and is updated in correspondence to the progress of the execution of the process instructions.

Generating a rendered image comprises: generating 904 a segmentation mask for an input 3D geometry; generating a plurality of texture maps associated with portions of the geometry; generating 906 a pl

Unsupervised volumetric 3D animation (UVA) of non-rigid deformable objects without annotations learns the 3D structure and dynamics of objects solely from single-view red / green / blue (RGB) videos and decomposes the single-view RGB videos into semantically meaningful parts that can be tracked and animated. Using a 3D autodecoder framework, paired with a keypoint estimator via a differentiable perspective-n-point (PnP) algorithm, the UVA model learns the underlying object 3D geometry and parts decomposition in an entirely unsupervised manner from still or video images. This allows the UVA model to perform 3D segmentation, 3D keypoint estimation, novel view synthesis, and animation. The UVA model can obtain animatable 3D objects from a single or a few images. The UVA method also features a space in which all objects are represented in their canonical, animation-ready form. Applications include the creation of lenses from images or videos for social media applications.

A system and method for viewing aerial imagery and derived content on a web-based imagery browser that can be accessed by at least two users simultaneously. In addition, a system for interactively generating 3D geometry data for a location imaged from multiple viewpoints.

Systems and methods for 3D image scanners for real-time dynamic 3D surface imaging are disclosed. Embodiments of the present systems and methods describe a system and method comprising a first and / or second camera, and a projector, and a processor, the projector projects structured light with a fringe pattern onto a 3D object, the processor is configured to extract a phase map and a texture image from the image, and the processor calculates depth information from the phase map. Embodiments further describe methods and systems to determine wrapped phase from the image using a Hilbert transform, and to generate absolute phase from the wrapped phase using a combination of a quality-guided path following algorithm, a two-wavelength phase unwrapping algorithm, or a Markov Random Field method, and to generate a phase map from the absolute phase to determine depth information of the 3D object. Algorithms using conformal mapping, optimal transport mapping, and Tschupmueller mapping are used to register and track captured 3D geometry surfaces.