Abstract: This application provides an image rendering method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product. The method comprises the following steps: acquiring texture data of a virtual object; performing differential fitting processing on the texture data to obtain fitted texture data; baking the fitted texture data into a texture image to obtain a rendered texture image; and performing, based on the rendered texture image, image rendering processing corresponding to the virtual.

Abstract: The described aspects and implementations enable vehicle light classification in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes, obtaining, by a processing device, first image data characterizing a driving environment of an autonomous vehicle (AV). The processing device may identify, based on the image data, a vehicle within the driving environment. The processing device may process the image data using one or more trained machine-learning models (MLMs) to determine a state of one or more lights of the vehicle and cause an update to a driving path of the AV based on the determined state of the lights.

Abstract: Systems and methods are provided for generating task progress values from digital video. A temporal sequence of frames of a digital video is shuffled to generate a shuffled plurality of video frames. A reordering input prompt is assembled to include data indicative of one or more tasks depicted being performed in the digital video and the shuffled plurality of video frames. The reordering input prompt is processed using a generative model to generate data indicative of one or more task progress values corresponding to one or more of the shuffled plurality of video frames. Each task progress value represents an amount of progress towards accomplishing the one or more tasks that is depicted in the corresponding video frame. The generated task progress values may be used for various purposes, such as training a separate model, including a robot control policy, or for data quality control.

Abstract: The described aspects and implementations enable vehicle light classification in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes, obtaining, by a processing device, first image data characterizing a driving environment of an autonomous vehicle (AV). The processing device may identify, based on the image data, a vehicle within the driving environment. The processing device may process the image data using one or more trained machine-learning models (MLMs) to determine a state of one or more lights of the vehicle and cause an update to a driving path of the AV based on the determined state of the lights.

Abstract: Provided are an active defense system and method for an unknown threat. The system includes an intelligent threat early-warning module (10), an unknown threat detection module (20) and a self-adaption defense processing module (30). The intelligent threat early-warning module (10) is configured to perform threat prediction on a power grid situation data set collected from a power information network in real time to obtain threat early-warning information and send the information to the unknown threat detection module (20). The unknown threat detection module (20) is configured to perform threat detection and analysis on collected unknown threat network data when receiving the threat early-warning information to generate a threat analysis report and send the report to the self-adaption defense processing module (30). The self-adaption defense processing module (30) is configured to trigger a defense processing operation corresponding to a preset threat defense strategy according to the threat analysis report.

Abstract: This application provides an image rendering method performed by an electronic device. The method includes: acquiring first texture data of a virtual object and conversion parameters corresponding to second texture data of the virtual object; performing, based on the conversion parameters and the first texture data, fitting rendering to obtain a fitted rendering image comprising the virtual object; updating the conversion parameters and the first texture data based on a rendering loss between the fitted rendering image and a reference rendering image comprising the virtual object , wherein the reference rendering image is a rendering image that is obtained by performing rendering based on the second texture data; and performing, based on the updated conversion parameters and the updated first texture data, real-time rendering to obtain a target rendering image comprising the virtual object.

Abstract: The present disclosure provides a method for processing visibility data of a three-dimensional model performed by a computer device. The method includes: obtaining original values of visibility data of a plurality of sampling points of the three-dimensional model; determining visibility data of a plurality of vertexes of the three-dimensional model based on a first error function measuring differences between recovery values of the visibility data of the sampling points from interpolation of the visibility data of the vertexes of the three-dimensional model and the original values of the visibility data of the sampling points and change rates of the recovery values of the visibility data of the sampling points; and storing the visibility data of each vertex of the three-dimensional model. This method reduces the space for storing the visibility data, relieves the pressure of storing the visibility data, and improves the efficiency of rendering the three-dimensional model.

Abstract: Provided are an active defense system and method for an unknown threat. The system includes an intelligent threat early-warning module (10), an unknown threat detection module (20) and a self-adaption defense processing module (30). The intelligent threat early-warning module (10) is configured to perform threat prediction on a power grid situation data set collected from a power information network in real time to obtain threat early-warning information and send the information to the unknown threat detection module (20). The unknown threat detection module (20) is configured to perform threat detection and analysis on collected unknown threat network data when receiving the threat early-warning information to generate a threat analysis report and send the report to the self-adaption defense processing module (30). The self-adaption defense processing module (30) is configured to trigger a defense processing operation corresponding to a preset threat defense strategy according to the threat analysis report.

Abstract: In a method for collision event determination, a first estimated trajectory that a target object is expected to follow during a next movement period is obtained. A second estimated trajectory that a collision object is expected to follow during the next movement period is obtained. A spatial positional relationship between the first estimated trajectory and the second estimated trajectory is determined to satisfy a reference collision condition. Based on the determination that the spatial positional relationship between the first estimated trajectory and the second estimated trajectory satisfies the reference collision condition, a candidate collision space that indicates a space through which the target object is not allowed to move is determined. A collision event is determined to occur between the target object and the collision object when a plurality of candidate object positions on the first estimated trajectory include a target object position in the candidate collision space.

Abstract: This application provides an image rendering method performed by an electronic device. The method includes: extracting a plurality of key frames from a video stream as input images of a target object; determining masks, reflection images, and camera parameters of the input images; constructing a model corresponding to the target object based on the masks and the camera parameters; mapping the model based on the reflection image, to obtain a map of the model; and performing illumination restoration on the model based on the map, to obtain an output image of the target object.

Abstract: Methods, systems, and apparatus for processing point clouds using neural networks to perform a machine learning task. In one aspect, a system comprises one or more computers configured to obtain a set of point clouds captured by one or more sensors. Each point cloud includes a respective plurality of three-dimensional points. The one or more computers assign the three-dimensional points to respective voxels in a voxel grid, where the grid of voxels includes non-empty voxels to which one or more points are assigned and empty voxels to which no points are assigned. For each non-empty voxel, the one or more computers generate initial features based on the points that are assigned to the non-empty voxel. The one or more computers generate multi-scale features of the voxel grid, and the one or more computers generate an output for a point cloud processing task using the multi-scale features of the voxel grid.

Abstract: Some implementations related to using a large language model (LLM) in generating (and potentially refining) a plan for the execution of a long-horizon robotic task. Various implementations include processing, using the LLM, a free-form natural language instruction and textual feedback to generate LLM output. In many implementations, the free-form natural language instruction describes the robotic task. In additional or alternative implementations, the textual feedback can include task-specific feedback, passive scene description feedback, active scene description feedback, one or more additional or alternative types of environmental feedback, and/or combinations thereof. In some implementations, the system can select one or more robotic skills to perform based on the LLM output.

Abstract: A method for applying a surface treatment to a plugged honeycomb body comprising porous wall includes: mixing particles of an inorganic material with a liquid vehicle and a binder material to form a liquid-particulate-binder stream; mixing the liquid-particulate-binder stream with an atomizing gas, directing the liquid-particulate-binder stream into an atomizing nozzle thereby atomizing the particles into liquid-particulate-binder droplets comprised of the liquid vehicle, he binder material, and the particles; conveying the droplets toward the plugged honeycomb body by a gaseous carrier stream, wherein the gaseous carrier stream comprises a carrier gas and the atomizing gas; evaporating substantially all of the liquid vehicle from the droplets to form agglomerates comprised of the particles and the binder material; depositing the agglomerates onto the porous walls of the plugged honeycomb body; wherein the deposited agglomerates are disposed on, or in, or both on and in, the porous walls.

Abstract: A method for applying a surface treatment to a plugged honeycomb body comprising porous wall includes: mixing particles of an inorganic material with a liquid vehicle and a binder material to form a liquid-particulate-binder stream; mixing the liquid-particulate-binder stream with an atomizing gas, directing the liquid-particulate-binder stream into an atomizing nozzle thereby atomizing the particles into liquid-particulate-binder droplets comprised of the liquid vehicle, the binder material, and the particles; conveying the droplets toward the plugged honeycomb body by a gaseous carrier stream, wherein the gaseous carrier stream comprises a carrier gas and the atomizing gas; evaporating substantially all of the liquid vehicle from the droplets to form agglomerates comprised of the particles and the binder material; depositing the agglomerates onto the porous walls of the plugged honeycomb body; wherein the deposited agglomerates are disposed on, or in, or both on and in, the porous walls.

Abstract: A device for advanced nitrogen and phosphorus removal in sewage treatment includes a pre-denitrification zone, an anaerobic zone, an anoxic zone, an aerobic zone, a sedimentation zone, a biological filtration zone, and a clear water zone, where a sludge return system is provided between the pre-denitrification zone and the sedimentation zone; a nitrification liquid return system is provided between the anoxic zone and the aerobic zone; a filler layer is provided in the biological filtration zone, and the filler layer divides a cavity in the biological filtration zone to form an upper water inlet cavity and a lower water outlet cavity; a backwash aeration pipe is provided in the water outlet cavity, and a backwash water outlet is formed in the water inlet cavity; and the backwash water outlet is connected to a sludge concentration and storage tank or the pre-denitrification zone.

Abstract: A method for upgrading client software includes receiving an upgrade instruction for client software deployed on a computing node, where the client software is used to access one or more storage nodes in the storage system based on a received input/output I/O instruction; performing I/O suspension on the received I/O instruction in response to the upgrade instruction; and upgrading the client software based on the upgrade instruction.

Abstract: A method of producing soot, including: combusting a first fuel stream and a first oxidizer at a burner face; combusting a second fuel stream and a second oxidizer at the burner face, wherein the second fuel stream and the second oxidizer are premixed in advance of the burner face and a second equivalence ratio of the second fuel stream and the second oxidizer is less than about 1; and combusting a silicon-containing fuel into a plurality of soot particles, wherein the second fuel stream and the second oxidizer are combusted between the first fuel stream and the silicon-containing fuel. Applying this method of producing soot to deposit a preform suitable for the manufacture of optical fibers.

Abstract: This application provides an image rendering method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product. The method comprises the following steps: acquiring texture data of a virtual object; performing differential fitting processing on the texture data to obtain fitted texture data; baking the fitted texture data into a texture image to obtain a rendered texture image; and performing, based on the rendered texture image, image rendering processing corresponding to the virtual.

Abstract: A bone model combination method includes obtaining a plurality of bone models, each of the bone models comprising a pixel set, and each bone model corresponding to a common skeleton. The method further includes determining, for two of the bone models, a set of common bones that is an intersection of bones included in both of the two bone models. The method further includes determining, for common bones in the intersection, common bone chains, generating common bone chain duplicates on the basis of the common bone chains, and binding the common bone chain duplicates at upper common bones of the common bone chains. The method further includes building a mapping between the common bone chains and a pixel set included in a corresponding bone model, and building a mapping between the common bone chain duplicates and a pixel set included in a corresponding bone model.

Abstract: This application discloses a soft rasterizing method and apparatus, a device, a medium, and a program product, belonging to the field of computer technologies.