Abstract: A simulation data augmentation method, a simulation data augmentation device and a simulation data augmentation terminal are provided according to embodiments of the present application. The method includes: acquiring a point cloud based on a plurality of frames, wherein the point cloud includes a plurality of obstacles labeled with real labeling data; dividing the point cloud into a plurality of preset regions, wherein each of the preset regions includes at least one obstacle; and adjusting the obstacle based on the real labeling data of the obstacle in the preset regions to obtain simulation data.

Abstract: Aspects of the invention include receiving an original work written in a first style to be transformed into a derivate work written in a second style. The transforming includes accessing a similarity data structure organized to include a phrase-similarity vector space of phrase vectors representing degrees of similarity between phrases in a corpus of works written in the second style. An original sentence of the original work that includes one or more original phrases is obtained. The original phrases are located in the phrase-similarity vector space. For each of the original phrases, a similar phrase from the phrase-similarity vector space is selected based on the selected similar phrase having a phrase-similarity index corresponding to the original phrase that is greater than a predetermined phrase-similarity index threshold. The derivative work is created by replacing the original phrases in the original work with the selected similar phrases. The derivative work is output.

Abstract: The present application provides a model training method and apparatus, and a prediction method and apparatus, and it relates to fields of artificial intelligence, deep learning, image processing, and autonomous driving. The model training method includes: inputting a first sample image of sample images into a depth information prediction model, and acquiring depth information of the first sample image; acquiring inter-image posture information based on a second sample image of the sample images and the first sample image; acquiring a projection image corresponding to the first sample image, at least according to the inter-image posture information and the depth information; and acquiring a loss function by determining a function for calculating a similarity between the second sample image and the projection image, and training the depth information prediction model using the loss function.

Abstract: A data augmentation method, device are provided according to embodiments of the present application. The method includes: acquiring a point cloud of a frame, the point cloud comprising a plurality of original obstacles; obtaining a plurality of position voids by removing the original obstacles from the point cloud, and filling the position voids to obtain a real background of the point cloud; arranging a plurality of new obstacles labeled by labeling data, in the real background of the point cloud; and adjusting the new obstacles based on the labeling data of the new obstacles to obtain layout data of the new obstacles. The amount of real data is increased, and a diversity of the real data is improved.

Abstract: A method for generating simulated point cloud data, a device, and a storage medium includes: acquiring at least one frame of point cloud data collected by a road collecting device in an actual environment without a dynamic obstacle as static scene point cloud data; setting, least one dynamic obstacle in a coordinate system matching the static scene point cloud data; simulating in the coordinate system, a plurality of simulated scanning lights emitted by a virtual scanner located at an origin of the coordinate system; updating the static scene point cloud data according to intersections of the plurality of simulated scanning lights and the at least one dynamic obstacle to obtain the simulated point cloud data comprising point cloud data of the dynamic obstacle; and at least one of adding a set noise to the simulated point cloud data, and, deleting point cloud data corresponding to the dynamic obstacle according to a set ratio.

Abstract: Embodiments of the present disclosure are directed to a method for generating simulated point cloud data, a device, and a storage medium. The method includes: acquiring at least one frame of point cloud data collected by a road collecting device in an actual environment without a dynamic obstacle as static scene point cloud data; setting, according to set position association information, at least one dynamic obstacle in a coordinate system matching the static scene point cloud data; simulating in the coordinate system, according to the static scene point cloud data, a plurality of simulated scanning lights emitted by a virtual scanner located at an origin of the coordinate system; and updating the static scene point cloud data according to intersections of the plurality of simulated scanning lights and the at least one dynamic obstacle to obtain the simulated point cloud data comprising point cloud data of the dynamic obstacle.

Abstract: A radar point cloud data processing method and device, an apparatus, and storage medium are provided, which are related to technical fields of radar point cloud, automatic driving, and deep learning. An implementation includes: determining a target location area where a target object is located by utilizing a target detection box in the radar point cloud data; removing each point of the target object in the target location area from the radar point cloud data; and adding an object model to the target location area. By applying embodiments of the present disclosure, richer radar point cloud data may be obtained by removing the target object from the radar point cloud data and adding the needed three-dimensional model to the target location area in the radar point cloud data.

Abstract: A method, an apparatus, a device, and a medium for calibrating a posture of a moving obstacle are provided. The method includes: obtaining a 3D map, the 3D map including first static obstacles; selecting a target frame of data, the target frame of data including second static obstacles and one or more moving obstacles; determining posture information of each of the one or more moving obstacles in a coordinate system of the 3D map; registering the target frame of data with the 3D map; determining posture offset information of the target frame of data in the coordinate system according to a registration result; calibrating the posture information of each of the one or more moving obstacles according to the posture offset information; and adding each of the one or more moving obstacles after the calibrating into the 3D map.

Abstract: In one embodiment, a method includes generating embeddings for social-networking entities by training the embeddings using a training algorithm, where an embedding corresponding to an entity represents a point in a d-dimensional embedding space, identifying a subset of entities having one or more common attributes that is not encoded in the generated embeddings, encoding, for each entity in the subset, values of the one or more common attributes into a j-dimensional additional embedding, creating, for each entity in the subset, a (d+j)-dimensional embedding by concatenating the generated d-dimensional embedding with the j-dimensional additional embedding, detecting a need to identify entities similar to a reference entity that is a member of the subset, computing k-nearest neighbors of an embedding corresponding to the reference entity in the (d+j)-dimensional embedding space, identifying entities corresponding to the computed k-nearest neighbors, and providing information regarding the corresponding entities

Abstract: The present disclosure provides a method and a device for generating a 3D scene map, a related apparatus and a storage medium. The method includes the following. At least two frames of point cloud data collected by a collection device is obtained. Data registration is performed on the at least two frames of point cloud data. First type of point cloud data corresponding to a movable obstacle is deleted from each frame of point cloud data and each frame of point cloud data is merged to obtain an initial scene map. Second type of point cloud data corresponding to a regularly shaped object is replaced with model data of a geometry model matching with the regularly object for the initial scene map to obtain the 3D scene map.

Abstract: A loudspeaker system includes a loudspeaker base and a loudspeaker peripheral that is independent of the loudspeaker base. The loudspeaker peripheral is shaped as a role figure. The loudspeaker base and the loudspeaker peripheral connect through a contact connection or a non-contact connection, and provide personalized voice data corresponding to the role figure when connected.

Abstract: Aspects of the invention include receiving an original work written in a first style to be transformed into a derivate work written in a second style. The transforming includes accessing a similarity data structure organized to include a phrase-similarity vector space of phrase vectors representing degrees of similarity between phrases in a corpus of works written in the second style. An original sentence of the original work that includes one or more original phrases is obtained. The original phrases are located in the phrase-similarity vector space. For each of the original phrases, a similar phrase from the phrase-similarity vector space is selected based on the selected similar phrase having a phrase-similarity index corresponding to the original phrase that is greater than a predetermined phrase-similarity index threshold. The derivative work is created by replacing the original phrases in the original work with the selected similar phrases. The derivative work is output.

Abstract: An obstacle distribution simulation method, device and terminal based on multiple models. The method can include: acquiring a point cloud, the point cloud including a plurality of obstacles labeled with real labeling data; extracting the real labeling data of the obstacles, and training a plurality of neural network models based on the real labeling data of the obstacles; extracting unlabeled data in the point cloud, inputting the unlabeled data into the neural network models, and outputting a plurality of prediction results. The plurality of prediction results can include a plurality of simulated obstacles with attribute data; selecting at least one simulated obstacle based on the plurality of prediction results; and inputting the attribute data of the selected simulated obstacle into the neural network models to obtain position coordinates of the simulated obstacle, and further obtain a position distribution of the simulated obstacle.

Abstract: An information interaction method is applied to a first terminal installed with an interaction program, the first terminal is connected to a second terminal installed with a voice player and a lighting device, and the method includes: acquiring, by the first terminal, at least one type of interaction information of voice information, text information and expression information and at least one interaction effect of a sound effect and a lighting effect that are corresponding to the interaction information; and sending acquired effect data to the second terminal according to the interaction information and the interaction effect, the second terminal displaying the effect data.

Abstract: Embodiments of the present invention relate to the field of internet technologies, and disclose a method for pushing application content and a related device and system. A main feature of the method for pushing application content is that application content included in an application can be updated or upgraded according to an operation behavior of a user on the application.

Abstract: A method and system for simulating a distribution of obstacles are provided. The method includes: acquiring a plurality of point clouds of a plurality of frames, wherein each point cloud includes a plurality of original obstacles; acquiring real labeling data of an acquisition vehicle, and obtaining data of a simulation position of the acquisition vehicle based on the real labeling data and a movement rule of the acquisition vehicle; determining the number of obstacles to be simulated based on the data of the simulation position of the acquisition vehicle; selecting the determined number of obstacles to be simulated, from a range with the simulation position of the acquisition vehicle as a center, wherein the range is less than or equal to a maximum scanning range of the vehicle; and acquiring real labeling data of the selected obstacles, and obtaining a position distribution of the selected obstacles.

Abstract: A simulation data augmentation method, a simulation data augmentation device and a simulation data augmentation terminal are provided according to embodiments of the present application. The method includes: acquiring a point cloud based on a plurality of frames, wherein the point cloud includes a plurality of obstacles labeled with real labeling data; dividing the point cloud into a plurality of preset regions, wherein each of the preset regions includes at least one obstacle; and adjusting the obstacle based on the real labeling data of the obstacle in the preset regions to obtain simulation data.

Abstract: A data augmentation method, device are provided according to embodiments of the present application. The method includes: acquiring a point cloud of a frame, the point cloud comprising a plurality of original obstacles; obtaining a plurality of position voids by removing the original obstacles from the point cloud, and filling the position voids to obtain a real background of the point cloud; arranging a plurality of new obstacles labeled by labeling data, in the real background of the point cloud; and adjusting the new obstacles based on the labeling data of the new obstacles to obtain layout data of the new obstacles. The amount of real data is increased, and a diversity of the real data is improved.

Abstract: An obstacle distribution simulation method, device and terminal based on multiple models. The method can include: acquiring a point cloud, the point cloud including a plurality of obstacles labeled with real labeling data; extracting the real labeling data of the obstacles, and training a plurality of neural network models based on the real labeling data of the obstacles; extracting unlabeled data in the point cloud, inputting the unlabeled data into the neural network models, and outputting a plurality of prediction results. The plurality of prediction results can include a plurality of simulated obstacles with attribute data; selecting at least one simulated obstacle based on the plurality of prediction results; and inputting the attribute data of the selected simulated obstacle into the neural network models to obtain position coordinates of the simulated obstacle, and further obtain a position distribution of the simulated obstacle.

Abstract: Embodiments of an obstacle distribution simulation method, device and terminal based on a probability graph are provided. The method can include: acquiring a plurality of point clouds of a plurality of frames; acquiring real labeling data of an acquisition vehicle at vehicle labeled positions, and acquiring data of a simulation position of the acquisition vehicle; determining the number of obstacles to be simulated at a position to be simulated; extracting real labeling data of the obstacles, and constructing a labeling data set; dividing the labeling data set into a plurality of grids and calculating occurrence probabilities of the plurality of obstacles; selecting the determined number of obstacles to be simulated according to the occurrence probabilities; and acquiring a position distribution of the selected obstacles to be simulated for the position to be simulated based on the real labeling data of the selected obstacles to be simulated.