WO2022148075A1

WO2022148075A1 - Virtual object clustering method and apparatus, and storage medium and electronic apparatus

Info

Publication number: WO2022148075A1
Application number: PCT/CN2021/122146
Authority: WO
Inventors: 王耀民
Original assignee: 成都完美时空网络技术有限公司
Priority date: 2021-01-08
Filing date: 2021-09-30
Publication date: 2022-07-14
Also published as: CN112337093B; CN113134230A; CN112337093A; CN113134230B

Abstract

A virtual object clustering method and apparatus, and a storage medium and an electronic apparatus. The method comprises: voxelizing virtual objects to be clustered in a current scene, so as to obtain an entity object corresponding to each virtual object to be clustered (S202); merging entity objects to obtain initial fusion objects, wherein the volume of a bounding volume obtained after the entity objects included in each initial fusion object are merged falls within a target threshold value range (S204); and clustering the initial fusion objects according to voxel data of the initial fusion objects, so as to obtain a target fusion object to act as a clustering result, wherein the volume of a bounding volume corresponding to a plurality of entity objects included in the target fusion object falls into the target threshold value range (S206). By means of the method, the technical problem of the accuracy of clustering virtual objects being relatively low is solved.

Description

Method, device, storage medium and electronic device for clustering virtual objects

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of the Chinese patent application filed on January 08, 2021, the application number is 202110025936.X and the title is "A method, device, storage medium and electronic device for clustering virtual objects", the entire content of which is Incorporated herein by reference.

technical field

The present invention relates to the field of computers, and in particular, to a method, device, storage medium and electronic device for clustering virtual objects.

Background technique

At present, there are more and more 3D (three dimensional, three-dimensional) large-scene games, and the requirements for game quality are getting higher and higher. In order to meet players' increasingly critical taste in games and higher and higher requirements for game quality, the popularity of MMO (Massive Multiplayer Online, massively multiplayer online games) and chicken-eating games has led to more and more 3D scenes in mobile games. big.

The existing HLOD (Hierachy Level-of-Detail, Hierarchical Level of Detail model) system usually uses a separate solution based on the bounding volume (bounding sphere or bounding box) to calculate automatic clustering, and merges by computing The method of dividing the volume of the back bounding volume by the percentage of the volume of the bounding volume before the merging to the volume of the bounding volume after the merging is used to estimate the priority of merging, that is, under the constraints of the size of the volume after merging, the object is preferentially merged into the estimated value In large clusters, automatic clustering is realized.

When using the bounding volume-based scheme, the results of automatic clustering are not ideal because of the large error between the bounding volume and the mesh shape of the displayed 3D object. In practical applications, there may be cases where the mesh volume is not large, but the bounding volume is large, such as the wall of a building, or a road surface, etc., resulting in objects being preferentially merged into the wall or road.

For the above problems, no effective solution has been proposed yet.

SUMMARY OF THE INVENTION

The present invention provides a method, device, storage medium and electronic device for clustering virtual objects, so as to at least solve the technical problem of low accuracy of clustering virtual objects in the related art.

According to a first aspect of the embodiments of the present invention, a method for clustering virtual objects is provided, including: voxelizing virtual objects to be clustered in a current scene, and obtaining a corresponding virtual object corresponding to each of the virtual objects to be clustered. merging the entity objects to obtain an initial fusion object, and the volume of the bounding volume after the entity objects included in each initial fusion object is merged falls within the target threshold range; and, according to the initial fusion object The initial fusion object is clustered with the voxel data of , and the target fusion object is obtained as the clustering result, and the volume of the bounding volume corresponding to the plurality of solid objects included in the target fusion object falls within the target threshold range.

According to a second aspect of the embodiments of the present invention, a virtual object clustering device is further provided, including: a voxelization module, configured to voxelize the virtual objects to be clustered in the current scene, to obtain each The entity object corresponding to the virtual object to be clustered; the merging module is used for merging the entity objects to obtain an initial fusion object, and the volume colony of the bounding volume after the entity objects included in each initial fusion object are merged into the target threshold range; the clustering module is used to cluster the initial fusion object according to the voxel data of the initial fusion object, and obtain the target fusion object as the clustering result, and the target fusion object includes a plurality of The volume of the bounding volume corresponding to the solid object falls within the target threshold range.

According to a third aspect of the embodiments of the present invention, a storage medium is further provided, where the storage medium includes a stored program, and the above method is executed when the program runs.

According to a fourth aspect of the embodiments of the present invention, an electronic device is further provided, including a memory, a processor, and a computer program/instruction stored on the memory, the processor implements the above-mentioned first program/instruction when the processor executes the computer program/instruction The steps of the method of the aspect.

According to a fifth aspect of the embodiments of the present invention, there is also provided a computer program product, including a computer program, which implements the steps of the method in the first aspect when the computer program is executed by a processor.

In the embodiment of the present invention, the virtual objects to be clustered in the current scene are voxelized to obtain the entity object corresponding to each virtual object to be clustered; the entity objects are merged to obtain the initial fusion object, each The volume of the bounding volume after merging the entity objects included in the initial fusion object falls within the target threshold range; the initial fusion object is clustered according to the voxel data of the initial fusion object, and the target fusion object is obtained as the clustering result. In the way that the volume of the bounding volume corresponding to the included multiple entity objects falls within the target threshold range, the virtual objects to be clustered in the current scene are subjected to voxelization processing to obtain entity objects, and the entity objects are merged to obtain satisfying clustering. Conditional initial fusion object, and then cluster the initial fusion object to obtain the target fusion object as the final clustering result. After voxelization of the virtual object, the voxel representation can more accurately reflect the volume of the grid , so that the obtained bounding volume of the object is more in line with the real shape of the model, achieves the purpose of reducing the error between the bounding volume and the displayed virtual object grid shape, and thus achieves the technical effect of improving the accuracy of clustering virtual objects , thereby solving the technical problem of low accuracy of clustering virtual objects.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description serve to explain the principles of the invention.

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. In other words, on the premise of no creative labor, other drawings can also be obtained from these drawings.

FIG. 1 is a schematic diagram of a hardware environment of a method for clustering virtual objects according to an embodiment of the present invention.

FIG. 2 is a schematic flowchart of a method for clustering virtual objects according to an embodiment of the present invention.

FIG. 3 is a schematic diagram of a virtual object voxelization according to an embodiment of the present invention.

FIG. 4 is a schematic diagram of a process of generating an initial MergeData object according to an optional embodiment of the present invention.

FIG. 5 is a schematic diagram of a process of generating clusters according to an optional embodiment of the present invention.

FIG. 6 is a schematic diagram of an apparatus for clustering virtual objects according to an embodiment of the present invention.

FIG. 7 schematically shows a structural block diagram of an electronic device for implementing the method according to the present invention.

Figure 8 schematically shows a structural block diagram of a computer program product implementing the method according to the present invention.

Detailed ways

In order to make those skilled in the art better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only Embodiments are part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be noted that the terms "first", "second" and the like in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used may be interchanged under appropriate circumstances such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

According to a first aspect of the embodiments of the present invention, a method for clustering virtual objects is provided.

In some embodiments, the above clustering method for virtual objects can be applied to the hardware environment formed by the terminal 101 and the server 103 as shown in FIG. 1 . As shown in FIG. 1 , the server 103 is connected to the terminal 101 through the network, and can be used to provide services (such as game services, application services, etc.) for the terminal or the client installed on the terminal, and a database can be set on the server or independently of the server, For providing data storage services for the server 103, the above-mentioned network includes but is not limited to: a wide area network, a metropolitan area network or a local area network, and the terminal 101 is not limited to a PC, a mobile phone, a tablet computer, and the like. The method for clustering virtual objects in this embodiment of the present invention may be executed by the server 103 , may also be executed by the terminal 101 , or may be executed jointly by the server 103 and the terminal 101 . Wherein, the terminal 101 performs the method for clustering virtual objects according to the embodiment of the present invention, and may also be performed by a client installed on the terminal 101 .

FIG. 2 is a schematic flowchart of a method for clustering virtual objects according to an embodiment of the present invention. As shown in FIG. 2 , the method may include: step S202 , step S204 and step S206 .

In step S202, the virtual objects to be clustered in the current scene are voxelized to obtain a physical object corresponding to each of the virtual objects to be clustered.

In step S204, the entity objects are merged to obtain an initial fusion object, and the volume of the bounding volume after the combination of the entity objects included in each of the initial fusion objects falls within the target threshold range.

In step S206, the initial fusion object is clustered according to the voxel data of the initial fusion object, and the target fusion object is obtained as the clustering result, and the bounding volumes corresponding to the plurality of entity objects included in the target fusion object are obtained. The volume falls within the target threshold range.

Through the above steps S202 to S206, the virtual objects to be clustered in the current scene are subjected to voxelization to obtain entity objects, and the entity objects are merged to obtain an initial fusion object that satisfies the clustering conditions, and then the initial fusion objects are divided into groups. cluster, and obtain the target fusion object as the final clustering result. After voxelizing the virtual object, the voxel representation can more accurately reflect the volume of the mesh, so that the obtained bounding volume of the object is more in line with the reality of the model. shape, to achieve the purpose of reducing the error between the bounding volume and the displayed virtual object grid shape, thereby achieving the technical effect of improving the accuracy of clustering virtual objects, and then solving the accuracy of clustering virtual objects. Less technical issues.

In some embodiments, the above clustering method for virtual objects may be applied to, but is not limited to, an HLOD (Hierachy Level-of-Detail, Hierarchical Level of Detail model) system to automatically perform virtual objects (such as 3D objects) in a scene. in the process of clustering.

In the technical solution provided in step S202, the current scene may include, but is not limited to, a game scene, an animation scene, a film and television scene, and the like. The current scene may be, but is not limited to, a 2D scene, a 3D scene, or a higher-dimensional scene, and so on.

In some embodiments, the virtual objects in the current scene may include, but are not limited to, various models in the scene, such as mountains, water, trees, buildings, facilities, characters, props, etc. in the game scene.

In some embodiments, the entity object corresponding to each virtual object to be clustered may include, but is not limited to, an Entity object and the like.

In some embodiments, voxelization is the transformation of a geometric representation of an object into a voxel representation closest to the object, resulting in a voxel data set that not only includes surface information of the model, but also describes the model's surface information. internal properties. FIG. 3 is a schematic diagram of voxelization of a virtual object according to an embodiment of the present invention. As shown in FIG. 3 , a voxelized wall surface of an L-shaped grid in a game scene is obtained by voxelization processing. After the model is voxelized, the voxel representation can more accurately reflect the volume of the mesh, and the number of small squares in the voxelized model can represent the volume of the mesh.

In some embodiments, the virtual objects to be clustered in the current scene are voxelized to obtain a physical object corresponding to each virtual object, including steps S11 to S14.

In step S11, a virtual object that is allowed to be clustered is obtained from the virtual objects included in the current scene as the virtual object to be clustered.

In step S12, the virtual object to be clustered is voxelized to obtain voxel data of the virtual object to be clustered.

In step S13, an initial entity object corresponding to the virtual object to be clustered is generated.

In step S14, the voxel data of the virtual object to be clustered is saved into the initial entity object, and the entity object corresponding to the virtual object to be clustered is obtained.

In some embodiments, the virtual objects to be clustered may be, but are not limited to, virtual objects in the scene that are allowed to be clustered. The virtual objects to be clustered can be identified automatically or manually selected by the operator.

In some embodiments, each voxel data of the virtual object to be clustered may only store an integer world coordinate, and a set of voxel data of the virtual object to be clustered, that is, a set of world coordinates, represents the location of the grid. world region.

Optionally, in this embodiment, all virtual objects to be clustered are voxelized, and then stored in a newly generated Entity object.

In the technical solution provided in step S204, falling within the target threshold range may be, but not limited to, a clustering restriction, and the target threshold range may be, but not limited to, an upper limit value, that is, after the entity objects included in each initial fusion object are merged The volume of the bounding volume cannot be too large. The target threshold range may also be, but is not limited to, a threshold range, that is, the volume of the bounding volume after merging the solid objects included in each initial fusion object cannot be too large or too small.

In some embodiments, the initial fusion object after merging the solid objects includes a group of virtual objects, and the smallest cube completely enclosing the group of virtual objects is the combined bounding volume. The volume size of the combined bounding volume must meet the restriction requirements. For example, there is an upper limit requirement, and the volume size of the combined bounding volume cannot be too large.

As an optional embodiment, combining the entity objects to obtain an initial fusion object includes steps S21 to S23.

In step S21, all the entity objects are traversed, and it is determined whether the volume of the bounding volume obtained by merging any two entity objects falls within the target threshold range.

In step S22, a fusion object is created for each pair of solid objects whose volumes of the combined bounding volume fall within the target threshold range.

In step S23, each pair of entity objects is saved into the one fusion object to obtain the initial fusion object.

In some embodiments, the fusion object may include, but is not limited to, a MergeData object.

In some embodiments, any two Entity objects are tested to determine whether the combined bounding volume satisfies the size limit of automatic clustering (ie, the above-mentioned target threshold range), and for each pair of Entity objects that meet the conditions, create a MergeData object, and save the pair of Entity objects in the MergeData object to get the initial MergeData object.

In some embodiments, a process of generating an initial MergeData object according to a virtual object to be clustered is provided. FIG. 4 is a schematic diagram of a process of generating an initial MergeData object according to an optional embodiment of the present invention, as shown in FIG. 4 . Collect the virtual objects that can be clustered in the scene as virtual objects to be clustered, perform voxelization processing on each virtual object, generate an Entity object for each virtual object, and record the voxel data of the virtual object in the In the Entity object, traverse all Entity objects, and judge whether any two Entity objects are merged, and judge whether the bounding volume satisfies the constraints (for example, whether it is less than the preset value) after the merger. If the constraints are not met, the MergeData object will not be generated. , if the constraints are met, record the two Entity objects in a MergeData object, get a MergeData object, voxelize the MergeData object, record the voxelized voxel data in its own object, and calculate the MergeData object The size of the number of voxels to get the initial MergeData object.

In the technical solution provided in step S206, the clustering process may, but is not limited to, use a data structure diagram, such as a depth-first traversal method, a breadth-first traversal method, and the like. Through the traversal process of the data structure graph, the reachable nodes and the nodes that meet the size limit after merging are put together to form a cluster, so as to obtain the clustering result.

In some embodiments, the voxel data of the initial fusion object may include, but is not limited to, the number of voxels of the initial fusion object (such as the number of voxels of the MergeData object calculated in FIG. voxel distance, etc.

In some embodiments, clustering the initial fusion object according to the voxel data of the initial fusion object, and obtaining the target fusion object as the clustering result includes steps S31 and S32.

In step S31, the initial fusion objects are merged according to the voxel data of the initial fusion objects to obtain candidate fusion objects, wherein the candidate fusion objects do not include common entity objects and each candidate fusion object The volume of the corresponding bounding volume falls within the target threshold range.

In step S32, a fusion object satisfying the target condition is selected from the candidate fusion objects as the target fusion object.

In some embodiments, the candidate fusion objects obtained after merging do not include common physical objects, and the volume of the bounding volume corresponding to each candidate fusion object falls within the target threshold range, that is, the process of preliminarily merging the initial fusion objects There are two merging conditions. The first condition is that the candidate fusion objects obtained after merging do not include common entity objects, and the second condition is that the volume of the bounding volume corresponding to each candidate fusion object after merging is less than a certain threshold, and will not is too big.

In some embodiments, after initially merging the initial fusion objects to obtain candidate fusion objects, the candidate fusion objects are further screened by using preset target conditions to be satisfied for clustering to obtain a final clustering result.

As an optional embodiment, combining the initial fusion objects according to the voxel data of the initial fusion objects to obtain a candidate fusion object includes steps S41 to S47.

In step S41, it is determined whether two entity objects included in the initial fusion object intersect according to the voxel data of the initial fusion object.

In step S42, initial fusion objects where the entity objects intersect are added to the first list, and initial fusion objects where the physical objects do not intersect are added to the second list.

In step S43, sort the first list and the second list respectively according to the number of voxels of the initial fusion objects from large to small, and obtain a third list corresponding to the first list and a corresponding second list the fourth list.

In step S44, the initial fusion objects including the public entity objects in the third list are merged until there is no initial fusion object including the public entity objects, and a fifth list is obtained, in which the initial fusion objects included in the fifth list are The volume of the bounding volume corresponding to the fusion object falls within the target threshold range.

In step S45, after adding the fourth list to the fifth list, a sixth list is obtained.

In step S46, the initial fusion objects including the public entity objects in the sixth list are merged until there is no initial fusion object including the public entity objects, and a seventh list is obtained, in which the initial fusion objects included in the seventh list are The volume of the bounding volume corresponding to the fusion object falls within the target threshold range.

In step S47, an initial fusion object that is valid in the seventh list and includes more than one entity object is determined as the candidate fusion object.

In some embodiments, the first list may be, but is not limited to, a list marked as ContactList, the second list may be, but not limited to, a list marked NoContactList, the third list is a list obtained after sorting the ContactList list, and the fourth list is a list of NoContactList The resulting list after sorting the list. A fifth list is obtained after merging the sorted ContactList lists. The sixth list is obtained by adding the NoContactList list to the ContactList list after the merge operation. After the sixth list is merged again, a seventh list is obtained, and the initial fusion objects that meet the preset conditions in the seventh list are candidate fusion objects.

In some embodiments, for the created MergeData object, it is determined whether the two Entity objects included therein intersect, and the MergeData objects intersected by the Entity objects are stored in ContactList, and the MergeData objects that are not intersected by the Entity objects are stored in NoContactList.

In some embodiments, according to the number of voxels contained in the two Entity objects in each initial fusion object, the ContactList and NoContactList are sorted in descending order, and the sorted ContactList and NoContactList are obtained as the third list and fourth list.

In some embodiments, the above two merging processes are that after the merging operation is performed on the ContactList, NoContactList is added to the back of the ContactList after the merging operation, and the merging operation is continued.

In some embodiments, after two merge operations are performed, an initial MergeData object that is valid and includes more than 1 entity objects is selected from the seventh list as a candidate fusion object. Valid objects may include, but are not limited to, all objects included in the list, objects marked as valid tags in the list, or objects not marked as invalid tags in the list.

In some embodiments, determining whether the two entity objects included in the initial fusion object intersect according to the voxel data of the initial fusion object includes steps S51 and S52.

In step S51, it is judged whether the coordinates stored in the voxels in the two entity objects included in the initial fusion object include the same coordinates.

In step S52, if the coordinates stored in the voxels in the two entity objects included in the initial fusion object include the same coordinates, it is determined that the two entity objects included in the initial fusion object intersect.

In some embodiments, whether the entity objects intersect may be determined by, but not limited to, by judging whether the same coordinates are stored in the two entity objects. For example, each Entity object stores a set of world coordinates through voxels. If in the voxel data stored in the two Entity objects of the initial MergeData object, the coordinates stored in the voxels in one Entity object are the same as those in the other Entity. If the coordinates stored in the voxels in the object are the same, the two Entity objects are considered to intersect.

In some embodiments, if the coordinates stored in the voxels in the two entity objects included in the initial fusion object do not include the same coordinates, it is determined that the two entity objects included in the initial fusion object do not intersect. The case of not including the same coordinates may be used as a method for determining that the two entity objects do not intersect, or other methods may also be used to determine that the two entity objects do not intersect, which is not limited in this embodiment.

In some embodiments, merging the initial fusion objects including the common entity objects in the third list includes steps S61 to S64.

In step S61, two initial fusion objects including common entity objects are acquired from the third list.

In step S62, it is determined whether the volume of the total bounding volume corresponding to the two initial fusion objects falls within the target threshold range.

In step S63, in the case that the volume of the total bounding volume corresponding to the two initial fusion objects falls within the target threshold range, add the non-public entity objects included in the initial fusion objects that are later in the ordering to the lower ordering The initial fusion objects in the previous order are deleted or marked as the target label used to indicate that the objects are invalid.

In step S64, in the case that the volume of the total bounding volume corresponding to the two initial fusion objects does not fall within the target threshold range, delete the common entity objects included in the initial fusion objects that are ranked later.

In some embodiments, the above process traverses the initial fusion objects in the third list in a depth-first traversal manner. The initial fusion objects in the third list can also be traversed in a breadth-first traversal manner.

In some embodiments, if any two initial MergeData objects in the third list include common entity objects, for example, one of them includes two Entity objects of A and B, and the other includes two Entity objects of B and C, it is necessary to Merge the two initial MergeData objects.

In some embodiments, initially fused objects that are fused lower in order may be deleted to indicate that they are invalid, or the target tag may be marked to indicate that they are invalid. If the deletion means that the object is invalid, it can be considered that all the initial fusion objects in the seventh list are valid initial fusion objects. If the method of marking the target label indicates that the object is invalid, it can be considered that the initial fusion object without the target label in the seventh list is a valid initial fusion object.

In some embodiments, the above-mentioned target tag may be, but is not limited to, an Unvalidated tag. The initial fusion object marked as Unvalidated is the fusion object that has been merged into other initial fusion objects.

In some embodiments, the merging rules of the above two merging processes may include, but are not limited to, the following: Rule 1: The initial fusion objects that are sorted later are merged into the initial fusion objects that are sorted earlier. Rule 2, when the two MergeData objects including the common Entity object no longer exist in the list, the merging ends.

In some embodiments, when merging, all the elements in the initial MergeData object with the lower index are merged into the initial MergeData object with the first index. If the merged initial MergeData object satisfies the clustering condition, the merged initial MergeData object will be marked as Unvalidated. If the merged initial MergeData object does not meet the clustering condition, the public Entity object will be removed from the initial MergeData with the index later. object is deleted. For example, the first initial MergeData object includes two Entity objects A and B, and the second initial MergeData object includes two Entity objects B and C, then merge C into the first initial MergeData object to obtain A, B, C The initial MergeData object for the three Entity objects.

If after C is merged into the first initial MergeData object, the bounding volume formed by the three Entity objects A, B and C exceeds the volume limit of the bounding volume by clustering, then C cannot be merged into the first initial MergeData object, At this point, the B element is removed from the second initial MergeData object, and the second initial MergeData object becomes an initial MergeData object that only includes C one Entity object.

If after C is merged into the first initial MergeData object, the bounding volume formed by the three Entity objects A, B, and C does not exceed the volume limit of the bounding volume by clustering, then C can be merged into the first initial MergeData object, The second initial MergeData object is marked as Unvalidated at this point.

In some embodiments, the initial fusion objects including the public entity objects in the sixth list are merged until there is no initial fusion object including the public entity objects, and the process of obtaining the seventh list is the same as the above-mentioned merging of the third list The process is similar and will not be repeated again.

As an optional embodiment, selecting a fusion object that satisfies the target condition from the candidate fusion objects as the target fusion object includes steps S71 and S72.

In step S71, it is determined whether each candidate fusion object satisfies the target condition.

In step S72, the target fusion object is obtained by removing fusion objects that do not meet the target condition from the candidate fusion objects. The target condition includes at least one of the following conditions: the number of voxels of each entity object in the fusion object is greater than the number of target voxels; the number of entity objects included in the fusion object is greater than the number of target entity objects; the number of entity objects included in the fusion object The voxel distance between them is smaller than the target distance.

In some embodiments, one or more of the limitation of the number of voxels, the limitation of the number of solid objects, and the limitation of the distance between voxels may be used as target conditions to filter the target fusion object.

In some embodiments, taking the limitation of voxel distance as an example, the voxel distance refers to the distance between two objects estimated by voxels. Suppose there are two virtual objects, and their corresponding two sets of voxel data are A and B, respectively. Compare the distance between each voxel in A and each voxel in B, and the closest one among all distances can be regarded as is the voxel distance between two objects. Assuming that after the clustering ends, the voxel distance of any two virtual objects in the cluster is greater than a threshold, then the result does not meet the clustering conditions, and the clustering can be directly eliminated from the clustering result.

In some embodiments, a threshold may also be set for the number of entity objects in each candidate fusion object, and when the candidate fusion objects are finally screened, candidate fusion objects that do not reach the threshold are eliminated.

In some embodiments, clustering the initial fusion object according to the voxel data of the initial fusion object, and obtaining the target fusion object as the clustering result includes steps S81 to S84.

In step S81, the initial fusion objects are merged according to the voxel data of the initial fusion objects to obtain the target fusion object.

In step S82, an initial clustering object is generated for each target fusion object.

In step S83, the entity objects included in each target fusion object are saved to the one initial clustering object to obtain the target clustering object.

In step S84, the target clustering object is added to the clustering list to obtain the clustering result.

In some embodiments, the clustering object may be, but is not limited to, a Cluster object. After obtaining the target fusion object, a Cluster object is generated for each target fusion object, and the entity objects in the target fusion object are saved in the Cluster object, and the All generated Cluster objects are stored in the ClusterList list, which is the result of automatic clustering.

In some embodiments, a process of generating clusters is provided. FIG. 5 is a schematic diagram of a process of generating clusters according to an optional embodiment of the present invention. As shown in FIG. 5 , each initial MergeData object is processed Intersection judgment, the judgment process can be: judging whether the voxels of the two Entity objects included in each initial MergeData object include the same coordinates, if so, add the initial MergeData object to the ContactList, if not, add the initial MergeData object to the NoContactList . After the above-mentioned intersection judgment process cycle ends, the initial fusion objects in the obtained two sets of Lists are sorted from large to small according to the number of voxels. After sorting, a depth-first merge is performed on the elements in ContactList, and NoContactList is not considered in the merge process. For the merged result, consider the elements in the NoContactList and perform a depth-first merge. The merged List is filtered, and the MergeData objects that meet the limit of the number of elements are converted into Cluster objects, and the List composed of all Cluster objects is the clustering result.

It should be noted that, for the sake of simple description, the foregoing method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence. As in accordance with the present invention, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

From the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk, CD-ROM), including several instructions to make an electronic device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention.

According to a second aspect of the embodiments of the present invention, there is also provided an apparatus for clustering virtual objects for implementing the above method for clustering virtual objects. FIG. 6 is a schematic diagram of an apparatus for clustering virtual objects according to an embodiment of the present invention. As shown in FIG. 6 , the apparatus may include: a voxelization module 62 , a merging module 64 and a clustering module 66 .

The voxelization module 62 is configured to voxelize the virtual objects to be clustered in the current scene, so as to obtain a physical object corresponding to each of the virtual objects to be clustered.

The merging module 64 is configured to merge the entity objects to obtain an initial fusion object, and the volume of the bounding volume after merging the entity objects included in each initial fusion object falls within the target threshold range.

The clustering module 66 is configured to perform clustering on the initial fusion object according to the voxel data of the initial fusion object, and obtain the target fusion object as the clustering result, corresponding to the plurality of entity objects included in the target fusion object The volume of the bounding volume falls within the target threshold range.

It should be noted that the voxelization module 62 in this embodiment may be used to perform step S202 in this embodiment of the present invention, and the merging module 64 in this embodiment may be used to perform step S204 in this embodiment of the present invention. The clustering module 66 in the embodiment may be used to perform step S206 in the embodiment of the present invention.

It should be noted here that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content of the inventions in the above embodiments. It should be noted that, as a part of the device, the above modules may run in the hardware environment as shown in FIG. 1 , and may be implemented by software or hardware.

Through the above modules, voxelize the virtual objects to be clustered in the current scene to obtain solid objects, merge the solid objects to obtain an initial fusion object that satisfies the clustering conditions, and then cluster the initial fusion objects to obtain the target The fusion object is used as the final clustering result. After the virtual object is voxelized, the voxel representation can more accurately reflect the volume of the mesh, so that the obtained bounding volume of the object is more in line with the real shape of the model, achieving The purpose of reducing the error between the bounding volume and the displayed virtual object mesh shape, thereby achieving the technical effect of improving the accuracy of clustering virtual objects, thereby solving the technology of low accuracy of clustering virtual objects question.

In some embodiments, the voxelization module includes: an acquisition unit, configured to acquire, from the virtual objects included in the current scene, a virtual object that allows clustering as the virtual object to be clustered; voxelization a unit for voxelizing the virtual object to be clustered to obtain voxel data of the virtual object to be clustered; a first generating unit for generating an initial value corresponding to the virtual object to be clustered an entity object; and a first saving unit, configured to save the voxel data of the virtual object to be clustered into the initial entity object to obtain the entity object corresponding to the virtual object to be clustered.

In some embodiments, the merging module includes: a judging unit for traversing all the entity objects, and judging whether the volume of the bounding volume after merging any two entity objects falls within the target threshold range; a creating unit, used for creating a fusion object for each pair of entity objects whose volumes of the combined bounding volume fall within the target threshold range; a second saving unit, configured to save the each pair of entity objects into the one fusion object, The initial fusion object is obtained.

In some embodiments, the clustering module includes: a first merging unit, configured to merge the initial fusion objects according to the voxel data of the initial fusion objects to obtain candidate fusion objects, and the candidate fusion objects are mutually Does not include public entity objects and the volume of the bounding volume corresponding to each candidate fusion object falls within the target threshold range; the screening unit is used for screening fusion objects that meet the target condition from the candidate fusion objects as the target fusion objects. Describe the target fusion object.

In some embodiments, the first merging unit is configured to: determine whether two entity objects included in the initial fusion object intersect according to the voxel data of the initial fusion object; add the initial fusion object intersected by the entity objects to the first list, and add the initial fusion objects whose entity objects do not intersect to the second list; sort the first list and the second list respectively according to the number of voxels of the initial fusion objects from large to small , obtain the third list corresponding to the first list and the fourth list corresponding to the second list; merge the initial fusion objects including the public entity objects in the third list until there is no entity including the public entity The initial fusion object of the object, a fifth list is obtained, and the volume of the bounding volume corresponding to the initial fusion object included in the fifth list falls within the target threshold range;

After the fourth list is added to the fifth list, a sixth list is obtained; the initial fusion objects including the public entity objects in the sixth list are merged, until there is no initial fusion including the public entity objects object, obtain a seventh list, the volume of the bounding volume corresponding to the initial fusion object included in the seventh list falls within the target threshold range; the number of valid and included entity objects in the seventh list is greater than The initial fusion object of 1 is determined as the candidate fusion object.

In some embodiments, the first merging unit is configured to: determine whether the coordinates stored in the voxels in the two entity objects included in the initial fusion object include the same coordinates; If the coordinates stored in the voxels in the two entity objects include the same coordinates, it is determined that the two entity objects included in the initial fusion object intersect.

In some embodiments, the first merging unit is configured to: acquire from the third list two initial fusion objects including common entity objects; determine the total bounding volume corresponding to the two initial fusion objects Whether the volume falls within the target threshold range; if the volume of the total bounding volume corresponding to the two initial fusion objects falls within the target threshold range, the non-public included in the initial fusion The entity object is added to the initial fusion object in the first order, and the initial fusion object in the lower order is deleted or marked as a target label for indicating that the object is invalid; in the total bounding volume corresponding to the two initial fusion objects If the volume does not fall within the target threshold range, the common entity objects included in the initial fusion objects that are later in the order are deleted.

In some embodiments, the screening unit is configured to: determine whether each candidate fusion object satisfies the target condition; remove fusion objects that do not meet the target condition from the candidate fusion objects to obtain the target fusion object; the target condition includes at least one of the following conditions: the number of voxels of each entity object in the fusion object is greater than the number of target voxels; the number of entity objects included in the fusion object is greater than the number of target entity objects; the fusion object The voxel distance between the solid objects included in is less than the target distance.

In some embodiments, the clustering module includes: a second merging unit for merging the initial fusion objects according to the voxel data of the initial fusion objects to obtain the target fusion object; a second generating unit , used to generate an initial clustering object for each target fusion object; the third storage unit is used to save the entity objects included in each target fusion object to the one initial clustering object, and obtain the target clustering object; an adding unit, used for adding the target clustering object to the clustering list to obtain the clustering result.

It should be noted here that the examples and application scenarios implemented by the foregoing modules and corresponding steps are the same, but are not limited to the contents disclosed in the foregoing embodiments. It should be noted that, as a part of the device, the above modules may run in the hardware environment as shown in FIG. 1 , and may be implemented by software or hardware, wherein the hardware environment includes a network environment.

Various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components of the virtual object clustering apparatus according to the embodiment of the present invention. The present invention can also be implemented as a program/instruction (eg, computer program/instruction and computer program product) for an apparatus or apparatus for performing some or all of the methods described herein. Such programs/instructions implementing the present invention may be stored on a computer readable medium, or may exist in the form of one or more signals, such signals may be downloaded from an Internet website, or provided on a carrier signal, or in any form Available in other formats.

Computer-readable media includes both persistent and non-permanent, removable and non-removable media, and storage of information may be implemented by any method or technology. Information may be computer readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cartridges, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices.

FIG. 7 schematically shows an electronic device that can implement the method for clustering virtual objects according to the present invention, the electronic device comprising a processor 710 and a computer-readable medium in the form of a memory 720 . Memory 720 is an example of a computer-readable medium having storage space 730 for storing computer programs/instructions 731 . When the computer program/instructions 731 are executed by the processor 710, the various steps in the method for clustering virtual objects described above can be implemented.

Figure 8 schematically shows a block diagram of a computer program product implementing the method according to the invention. The computer program product includes a computer program/instructions 810 that, when executed by a processor, such as the processor 710 shown in FIG. 7, enable the clustering of virtual objects described above steps in the method.

Specific embodiments of this specification have been described above, and other embodiments are intended to be included within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that in the embodiments and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily follow the specific order shown, or sequential order, to achieve desirable results. In certain embodiments, multitasking and parallel processing are also possible or advantageous.

It should also be noted that the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device comprising a series of elements includes not only those elements, but also Other elements not expressly listed, or which are inherent to such a process, method, article of manufacture, or apparatus are also included. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article of manufacture, or device that includes the element.

It should be understood that the above-mentioned embodiments are only for the purpose of illustrating the present invention and not for limiting the present invention. Those skilled in the art can also implement the present invention in other ways without departing from the basic spirit and characteristics of the present invention. The scope of the present invention shall be determined by the appended claims, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be covered therein.

Claims

A clustering method for virtual objects, comprising:

voxelizing the virtual objects to be clustered in the current scene, to obtain a physical object corresponding to each of the virtual objects to be clustered;

The entity objects are merged to obtain an initial fusion object, and the volume of the bounding volume after the entity objects included in each initial fusion object is merged falls within the target threshold range;

The initial fusion object is clustered according to the voxel data of the initial fusion object, and the target fusion object is obtained as the clustering result. The volume of the bounding volume corresponding to the plurality of entity objects included in the target fusion object falls within the target threshold range.
The method according to claim 1, wherein, voxelizing the virtual objects to be clustered in the current scene, and obtaining the entity object corresponding to each virtual object comprises:

Acquiring virtual objects that allow clustering from virtual objects included in the current scene as the virtual objects to be clustered;

voxelizing the virtual object to be clustered to obtain voxel data of the virtual object to be clustered;

generating an initial entity object corresponding to the virtual object to be clustered;

The voxel data of the virtual object to be clustered is saved into the initial entity object, and the entity object corresponding to the virtual object to be clustered is obtained.
The method according to claim 1, wherein combining the entity objects to obtain an initial fusion object comprises:

Traverse all the entity objects, and determine whether the volume of the bounding volume after the merger of any two entity objects falls within the target threshold range;

creating a fusion object for each pair of solid objects whose volumes of the combined bounding volume fall within the target threshold range;

Saving each pair of entity objects into the one fusion object to obtain the initial fusion object.
The method according to claim 1, wherein the initial fusion object is clustered according to the voxel data of the initial fusion object, and obtaining the target fusion object as a clustering result comprises:

The initial fusion objects are merged according to the voxel data of the initial fusion objects to obtain candidate fusion objects. The candidate fusion objects do not include common entity objects and each candidate fusion object corresponds to the bounding volume of the volume falls within the target threshold range;

A fusion object satisfying the target condition is selected from the candidate fusion objects as the target fusion object.
The method according to claim 4, wherein combining the initial fusion objects according to the voxel data of the initial fusion objects to obtain candidate fusion objects comprises:

Determine whether two entity objects included in the initial fusion object intersect according to the voxel data of the initial fusion object;

adding the initial fusion objects where the entity objects intersect to the first list, and adding the initial fusion objects where the entity objects do not intersect to the second list;

Sort the first list and the second list respectively according to the number of voxels of the initial fusion object from large to small, and obtain a third list corresponding to the first list and a fourth list corresponding to the second list ;

The initial fusion objects including the public entity objects in the third list are merged, until there is no initial fusion object including the public entity objects, and a fifth list is obtained, and the initial fusion objects included in the fifth list correspond to The volume of the bounding volume falls within the target threshold range;

After adding the fourth list to the fifth list, a sixth list is obtained;

Merge the initial fusion objects including the public entity objects in the sixth list until there is no initial fusion object including the public entity objects, and obtain the corresponding initial fusion objects included in the seventh list of the seventh list. the volume of the bounding volume falls within the target threshold range;

An initial fusion object that is valid in the seventh list and includes more than 1 entity object is determined as the candidate fusion object.
The method according to claim 5, wherein determining whether two entity objects included in the initial fusion object intersect according to the voxel data of the initial fusion object comprises:

Determine whether the coordinates stored in the voxels in the two entity objects included in the initial fusion object include the same coordinates;

When the coordinates stored in the voxels in the two entity objects included in the initial fusion object include the same coordinates, it is determined that the two entity objects included in the initial fusion object intersect.
The method according to claim 5, wherein merging the initial fusion objects including the common entity objects in the third list comprises:

Obtain two initial fusion objects including common entity objects from the third list;

Determine whether the volume of the total bounding volume corresponding to the two initial fusion objects falls within the target threshold range;

In the case that the volume of the total bounding volume corresponding to the two initial fusion objects falls within the target threshold range, the non-public entity objects included in the initial fusion objects in the lower order are added to the initial fusion objects in the higher order. object, and delete or mark the initial fusion object that is later in the order as the target label used to indicate that the object is invalid;

In the case that the volume of the total bounding volume corresponding to the two initial fusion objects does not fall within the target threshold range, the common entity objects included in the initial fusion objects that are ranked later are deleted.
The method according to claim 4, wherein, selecting a fusion object that satisfies a target condition from the candidate fusion objects as the target fusion object comprises:

determining whether each candidate fusion object satisfies the target condition;

Eliminating the fusion object that does not meet the target condition from the candidate fusion object to obtain the target fusion object;

The target condition includes at least one of the following conditions:

The number of voxels of each entity object in the fusion object is greater than the number of target voxels;

The number of entity objects included in the fusion object is greater than the number of target entity objects;

The voxel distance between the solid objects included in the fusion object is smaller than the target distance.
The method according to claim 1, wherein the initial fusion object is clustered according to the voxel data of the initial fusion object, and obtaining the target fusion object as a clustering result comprises:

Merging the initial fusion objects according to the voxel data of the initial fusion objects to obtain the target fusion object;

Generate an initial clustering object for each target fusion object;

The entity objects included in each of the target fusion objects are saved to the initial clustering object to obtain the target clustering object;

The target clustering object is added to the clustering list to obtain the clustering result.
A device for clustering virtual objects, comprising:

A voxelization module, configured to voxelize the virtual objects to be clustered in the current scene, to obtain a physical object corresponding to each of the virtual objects to be clustered;

a merging module, used for merging the entity objects to obtain an initial fusion object, and the volume of the bounding volume after the merging of the entity objects included in each of the initial fusion objects falls within the target threshold range;

The clustering module is used to cluster the initial fusion object according to the voxel data of the initial fusion object, and obtain the target fusion object as the clustering result, and the corresponding objects of the plurality of entity objects included in the target fusion object are obtained. The volume of the bounding volume falls within the target threshold range.
A storage medium, the storage medium comprising a stored program, the program executes the method according to any one of the preceding claims 1 to 9 when the program runs.
An electronic device comprising a memory, a processor and a computer program stored on the memory, the processor implementing the steps of the method according to any one of claims 1 to 9 when the processor executes the computer program.
A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 9.