Abstract: The present disclosure relates to an integrated channel apparatus for a non-heat pump thermal management integrated module and an electric vehicle. The integrated channel apparatus includes a first component and a second component, the first component is provided with a channel group that runs through the first component along a thickness direction and a groove that does not run through the first component, the channel group and the groove are used to accommodate a connecting tube. The second component is provided with multiple strip grooves, each strip groove is provided corresponding to two channels to connect the two channels. During use, the integrated channel apparatus is installed in the non-heat pump thermal management integrated module, and the connecting tubes that need to be connected are placed in the two channels and the strip groove correspondingly connecting the two channels.

Abstract: A method for approximating an inverse Hessian is provided. One methodology to generate the inverse Hessian is to precondition the gradient, such as by using point-spread function deconvolution, T-power, or source-illumination compensation, prior to using non-stationary matching filters (NMF) to generate the inverse Hessian. Various types of NMF are contemplated, including using filters for different windows in the subsurface or using filters assigned to specific locations in the subsurface. Further, the number of filters for NMF may vary from iteration to iteration. For example, the filters assigned to the specific locations in the subsurface may be generated in a multi-scale manner, in which an initial iteration uses longer scale/longer wavelength features for inversion and subsequent iterations use finer scale/smaller wavelength features for inversion.

Abstract: A scene generation method is disclosed, which comprises: controlling, according to an execution request for a target scene, a vehicle to enter a safety preparation state for implementing the target scene (S101); analyzing a script of the target scene, to generate a scene execution strategy for a respective scene execution component of the vehicle (S102), wherein the scene execution strategy includes an execution function and execution time information of the respective scene execution component; and triggering the respective scene execution component to work according to the scene execution strategy, to generate the target scene (S103). A scene generation apparatus, system and device as well as a storage medium are disclosed. According to the method, under the condition of ensuring safety and controllability of the vehicle, more diverse scenes are provided to a user on the vehicle side, and the entertainment and application experience of the vehicle are improved.

Abstract: An object association method, apparatus and system, and a storage medium are provided. The method includes: obtaining a first image and a second image; and determining an association relationship between objects in the first image and objects in the second image based on surrounding information of the objects in the first image and surrounding information of the objects in the second image, where the surrounding information of one object is determined according to pixels within a set range around a bounding box of the object in the image where the object is located.

Abstract: Method and apparatus for generating point cloud encoder, method and apparatus for generating point cloud data, electronic device and computer storage medium are provided. The method for generating point cloud encoder includes: first point cloud data and second point cloud data of an object are acquired; a first probability distribution of a global feature of the first point cloud data is determined based on a first encoder; a second probability distribution of a global feature of the second point cloud data is determined based on a second encoder; a weight of the first encoder is regulated based on a first difference between the first probability distribution and the second probability distribution to obtain a target weight of the first encoder; and a point cloud encoder is generated according to the first encoder and the target weight.

Abstract: Disclosed is a method and apparatus for point cloud data processing, an electronic device and a computer storage medium. For each of multiple data points of first point cloud data, initial feature information of a respective one of the multiple data points and initial feature information of each of multiple neighboring points of the respective data point taken as a center point are acquired; correlation degree information between the respective data point and the multiple neighboring points is determined based on the initial feature information of the respective data point and the initial feature information of each of the multiple neighboring points; first target feature information of the respective data point is determined based on the correlation degree information; and point cloud data reconstruction is performed based on the first target feature information of each of the multiple data points to obtain second point cloud data.

Abstract: A method and apparatus for processing point cloud data, a device, and a storage medium are provided. The method includes: determining, from first point cloud data acquired, multiple groups of neighbouring points for a to-be-processed point, wherein each group of neighbouring points among the multiple groups of neighbouring points has a respective different scale; for each group of neighbouring points, determining a respective association relationship between the group of neighbouring points and the to-be-processed point; for each group of neighbouring points, determining a respective association feature of the to-be-processed point based on the respective association relationship; determining a target feature of the to-be-processed point based on association features corresponding to the multiple groups of neighbouring points; and performing, based on target features of multiple to-be-processed points, point cloud completion on the first point cloud data to generate second point cloud data.

Abstract: Embodiments of the present disclosure provide a method and apparatus for point cloud completion, a network training method and apparatus, a device, and a storage medium. The method includes: determining a probability distribution of an acquired first point cloud; completing the first point cloud based on the probability distribution to obtain a primary completed point cloud; concatenating the primary completed point cloud and the first point cloud to obtain a concatenated point cloud; determining association relationships between the concatenated point cloud and multiple groups of neighbouring points of the concatenated point cloud; completing the concatenated point cloud based on the association relationships to obtain a second point cloud from completion the first point cloud.

Abstract: The embodiments of the present disclosure provide a method, an apparatus and a system for generating a point cloud completion network. The method includes: acquiring first point cloud data by inputting a latent space vector into a pre-trained first point cloud completion network; acquiring real point cloud data collected for a real object in a physical space; for a real point in the real point cloud data, selecting a preset number of points adjacent to the real point from the first point cloud data as neighbor points of the real point; generating second point cloud data based on the neighbor points in the first point cloud data of a plurality of real points; acquiring a second point cloud completion network by adjusting the first point cloud completion network based on a difference between the second point cloud data and the real point cloud data.

Abstract: The embodiments of the present disclosure provide a method, an apparatus and a system for completing point cloud data and a method, an apparatus and a system for processing point cloud data. The method includes: acquiring first point cloud data; acquiring second point cloud data by completing the first point cloud data with a point cloud completion network, wherein, the point cloud completion network is trained based on complete point cloud data and configured to generate third point cloud data according to target latent space vector, and a difference between fourth point cloud data acquired from the third point cloud data by performing preset degradation and real point cloud data collected from a physical space is within a preset difference range.

Abstract: The embodiments of the present disclosure provide methods and apparatuses for generating point cloud completion network and methods, apparatuses and systems for processing point cloud data. First point cloud data is acquired from a first point cloud completion network based on one or more latent space vectors that are acquired through sampling in latent space, and a second point cloud completion network is generated by adjusting the first point cloud completion network based on a points-distribution feature of the first point cloud data. Since the points-distribution feature of the point cloud data is taken into consideration during generating the second point cloud completion network, the trained second point cloud completion network is capable of correcting the points-distribution feature of the point cloud data, and thus outputting the point cloud data with a relatively even points-distribution feature.

Abstract: An object association method, apparatus and system, and a storage medium are provided. The method includes: obtaining a first image and a second image; and determining an association relationship between objects in the first image and objects in the second image based on surrounding information of the objects in the first image and surrounding information of the objects in the second image, where the surrounding information of one object is determined according to pixels within a set range around a bounding box of the object in the image where the object is located.