Virtual scene object group selection method based on probability propagation and position rearrangement

Abstract

The embodiment of the invention discloses a virtual scene object group selection method based on probability propagation and position rearrangement. A specific embodiment of the method comprises the following steps: constructing a space grid to accelerate a probability propagation process, querying a minimum side length value of a collision body in all objects, and setting the minimum side length value as the side length of a minimum unit of the space grid; presetting a grouping center position so as to ensure that the positions of the grouping center positions are maximum values; recording a grouping probability vector for each object; detecting a selection operation of a user for ungrouped objects; based on a probability propagation algorithm of two strategies, a grouping probability vector is updated, and grouping priorities of the remaining to-be-selected objects are determined; and relocating the position of the target object based on an object position rearrangement algorithm of a grouping probability vector. According to the embodiment, the speed of classifying the randomly arranged multiple objects in the virtual reality scene by the user can be increased, the operation frequency and difficulty are reduced, and the user experience is improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of virtual reality interaction technology, and in particular to a method for selecting a virtual scene object group based on probability propagation and position rearrangement. Background Art

[0002] The interaction between users and virtual objects is at the core of creating an immersive interactive experience in a virtual reality environment. Group selection, as one of the hot topics in this field, is a key component. Group selection is an important means of object selection, allowing users to quickly group multiple objects and significantly improve the efficiency of operations on multiple types of objects.

[0003] At present, the object selection solutions for virtual reality are mainly divided into single object selection and multi-object selection. They include ray-based single object selection solutions, as well as multi-object selection solutions such as multi-finger ray selection, point cloud selection and lasso selection. Group selection was first proposed in the study of two-dimensional screen virtual environments for three-dimensional modeling applications. Unlike the extensive exploration of the first two object selection solutions, few people have studied group selection solutions in virtual environments before. Some researchers have performed post-alignment tasks on grouped objects.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0005] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present disclosure propose a virtual scene object group selection method based on probability propagation and position rearrangement to solve one or more of the technical problems mentioned in the above background technology section.

[0007] Some embodiments of the present disclosure provide a method for selecting a group of virtual scene objects based on probability propagation and position rearrangement, the method comprising: constructing a spatial grid to accelerate the calculation of the probability propagation process, querying the minimum side length of the collision body among all objects and setting it as the side length of the smallest unit of the spatial grid; pre-setting the grouping center position to ensure that the positions are as far away from each other as possible, making it more convenient for subsequent users to select and reducing the occurrence of misselection; recording a grouping probability vector for each object, updating the grouping probability vector based on a probability propagation algorithm of two strategies, and determining the grouping priority of the remaining objects to be selected; an object position rearrangement algorithm based on the grouping probability vector to reposition the position of the target object, reduce the randomness of the layout, and facilitate the next round of selection. Among them, the above two strategies are neighbor exclusion and interval transfer; the main content of the above neighbor exclusion strategy is: after the user makes a batch selection, the objects adjacent to the boundary of the selected object often belong to different groups, so the probability that these objects belong to the group to which the selected object belongs should be very low; the main content of the above interval transfer strategy is: for grouped objects, the probability that the group of ungrouped objects closest to it belongs to the same group as it decreases, and the probability that the group of ungrouped objects next to it belongs to the same group as it increases. At the same time, the influence of the grouped object on the adjacent ungrouped objects is limited by a distance threshold, and the influence should decrease as the distance between the two increases. If the distance threshold is exceeded, the influence is reduced to 0; the probability propagation algorithm based on the above two strategies performs the following steps: the grouping of the grouped objects The probability vector is set to a standard unit vector and will not be changed thereafter; a queue similar to that used in the breadth-first search algorithm is created and maintained. First, all grouped objects are added to the queue. For each group of objects in the same group, each adjacent layer of grids is traversed outward, and the ungrouped objects that are traversed are added to the queue; starting from the first ungrouped object in the queue, its grouping probability vector is updated; the grouping probability vector of the object is amplified to ensure that the effect of probability propagation is significant; the above position rearrangement algorithm follows the following steps: probability truncation is performed, a truncation threshold is set, and probability components below the threshold are set to 0, and are not involved in the calculation of the new position of the object; the new position of the object is obtained by performing a weighted mean calculation based on the grouping probability vector of the object and the center position of each group.

[0008] The embodiments of the present disclosure have the following beneficial effects:

[0009] In the embodiments of the present disclosure, object group selection is defined as a three-dimensional selection task, which selects multiple objects and classifies them into their respective groups. The difference between group selection and multi-object selection is that multi-object selection only selects multiple targets from a pile of objects to be selected, while group selection selects all objects from a pile of objects separately and classifies them. The group selection process may include single and multiple object selection processes. For complex virtual scenes with scattered objects, the two selection methods may be inefficient due to different selection environments. The embodiments of the present disclosure will focus on introducing new strategies and optimization algorithms to explore new group selection methods, and use the high efficiency of multi-object selection in complex environments to achieve fast object group selection.

[0010] Through the group selection method based on probability propagation and position rearrangement of the embodiment of the present disclosure, the number of user operations is effectively reduced, thereby speeding up the group selection process, effectively reducing the user workload, and improving the system availability. In terms of improving the efficiency of group selection, the embodiment of the present disclosure mainly increases the number of batch selections in the group selection process through probability propagation and object rearrangement clustering. Each round of position rearrangement will improve the overall order of the object, making it easier for users to use batch selection to select object groups. In addition, when the number of groups is larger, the time to complete the object grouping task in the embodiment of the present disclosure is more significantly increased compared to the existing method. This is because the more the number of groups, the more dispersed the distribution of objects in the scene, and the more difficult it is to use batch selection alone. In terms of the number of operations, the embodiment of the present disclosure includes batch selection, and the number of operations is less than that of the method using single object selection. At the same time, due to the object orderliness improved by position rearrangement, the efficiency of single batch selection is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0012] Figure 1 is a flowchart of some embodiments of a method for selecting a virtual scene object group based on probability propagation and position rearrangement according to the present disclosure.

[0013] Figure 2 It is a schematic diagram of rearrangement layout according to some embodiments of the virtual scene object group selection method based on probability propagation and position rearrangement disclosed in the present invention. DETAILED DESCRIPTION

[0014] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0015] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0016] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0017] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0018] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0019] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0020] Figure 1 The flowchart 100 of some embodiments of the method for selecting a virtual scene object group based on probability propagation and position rearrangement according to the present disclosure is shown. The method for selecting a virtual scene object group based on probability propagation and position rearrangement comprises the following steps:

[0021] Step 101, construct a spatial grid to accelerate the probability propagation process, query the minimum side length of the collision body among all objects, and set it as the side length l of the minimum unit of the spatial grid min Since it is very time-consuming to construct a search graph for all objects, the embodiment of the present disclosure chooses to construct a spatial grid for indexing all objects in the space, while accelerating the calculation of the probability propagation process. First, the total bounding box of the n objects to be selected is calculated, and then the minimum side length of the collision body among all objects is set to the side length l of the smallest unit of the spatial grid. min , which ensures that each minimum unit contains at most one object.

[0022] Step 102: Preset the group center positions to ensure that the positions between them are at the maximum value. Embodiments of the present disclosure tend to set the center on the total bounding box, as Figure 2 shown. According to the different number of groups m to which the objects to be grouped belong, four layouts for object rearrangement are designed. These four center layouts are respectively applicable to the scenarios of 0 < m ≤ 4, 4 < m ≤ 8, 8 < m ≤ 12, and 12 < m ≤ 16. When m > 16, the user is allowed to manually specify a new group center.

[0023] Step 103: Record a grouping probability vector p = (p1, p2,..., p m ) for each object. Here, m is the number of groups, and p i is the probability of predicting that the object belongs to the i-th group. Initially, set the grouping probability vectors of all objects to

[0024] Step 104: Detect the user's selection operation for ungrouped objects. According to the different degrees of orderliness of the object distribution in the scene, the user can use point selection and batch selection to select objects.

[0025] Step 105: Update the grouping probability vector based on the probability propagation algorithm of two strategies to determine the grouping priority of the remaining objects to be selected. Among them, the above-mentioned probability propagation algorithm based on two strategies includes proximity exclusion and interval transmission. Proximity exclusion can be: after the user makes a batch selection, the objects adjacent to the boundaries of the selected objects often belong to different groups, so the probability that these objects belong to the group to which the selected objects belong should be very low; interval transmission can be: for grouped objects, the probability that the nearest ungrouped object to it belongs to the same group as it decreases, and the probability that the second nearest ungrouped object to it belongs to the same group as it increases. At the same time, the influence of the grouped object on the adjacent ungrouped object is limited by a distance threshold, and the influence should decrease as the distance between the two increases. When the distance exceeds the distance threshold, the influence is reduced to 0.

[0026] The specific practical steps are as follows:

[0027] First step: Initialization: Set the grouping probability vector of the grouped object to the standard unit vector e k , that is, set the corresponding component of the grouping probability vector in this group to 1, and the remaining components to 0. Here, k is the number of the group to which the object belongs, and it will not be changed hereafter.

[0028] The second step is queue maintenance: create and maintain a queue similar to the one used in the breadth-first search algorithm. First, add all grouped objects to the queue. For each group of objects in the same group, traverse each adjacent grid layer outward and add the ungrouped objects traversed to the queue. This process continues until all ungrouped objects enter the queue. Grouped objects have the highest priority. The closer the level of an ungrouped object is to a grouped object, the higher its priority in the queue.

[0029] Step 3, propagation: Starting from the first ungrouped object in the queue, update its grouping probability vector using the following formula:

[0030]

[0031] Where j = i-1, P i is the probability vector of grouping the i-th object in the queue, ⊙ is the Hadamard product, σ ij is the distance between the i-th object and the j-th object in the queue, is a vector of all 1s, and is obtained using the following formula:

[0032]

[0033] Among them, (x i ,y i , z i ) is the three-dimensional position coordinate of the i-th object in the virtual environment, and λ is a parameter defined by the user;

[0034] The essence of probability propagation is the distance descriptor σ ij On the one hand, when the distance between the i-th and j-th objects increases, σ ij Increase, while Increase, and then P i This reflects the neighbor exclusion strategy. On the other hand, according to the above analysis, σ ij The closer the distance between two objects is, the greater the difference between their grouping probabilities. This reflects the interval transfer strategy.

[0035] The fourth step is probability amplification: amplify the grouping probability vector of the object to ensure that the effect of probability propagation is significant. Note γ=max(α1,α2,...,α m ), there is an amplification function:

[0036]

[0037] Among them, A() is the amplification function, β is the amplification coefficient, which is a variable parameter, and its value is taken as 0.8 here. is the grouping probability vector, γ is The infinite norm of The maximum value among the elements in .

[0038] Step 106 : relocate the position of the target object based on the object position rearrangement algorithm of the grouping probability vector.

[0039] The first step is probability truncation: Due to the existence of low-probability components in the grouping probability vector, the effect of directly using the original grouping probability vector to calculate the new position of the object is not significant. Therefore, a truncation threshold is set to set the probability components below the threshold to 0 and not participate in the calculation of the new position of the object. For the grouping probability vector of object i, There is a truncation function:

[0040]

[0041] in, is the i-th component of the new object grouping probability vector, med(α) is the median of each component of the grouping probability vector;

[0042] The second step is interpolation calculation: the new position of the object is calculated by weighted mean calculation based on the group probability vector of the object and the center position of each group. The new position of the object H (x, y, z) is obtained by the following formula:

[0043]

[0044] Among them, C i is the center position of each group, is the i-th component of the probability vector of the new grouping of objects;

[0045] In response to the determined new position where there is an object, a breadth-first search algorithm is used with the position as the center to find the nearest empty space as the new position of the object.

[0046] After step 106, the following steps may also be included:

[0047] Determine whether all objects have been grouped. If all objects have been grouped, the process ends. If not, loop through steps 104, 105, 106, and 107.

[0048] The embodiments of the present disclosure have the following beneficial effects: through the group selection method based on probability propagation and position rearrangement of the embodiments of the present disclosure, the number of user operations is effectively reduced, thereby speeding up the group selection process, effectively reducing the workload of users, and improving the system availability. In terms of improving the efficiency of group selection, the embodiments of the present disclosure mainly increase the number of batch selections in the group selection process through probability propagation and object rearrangement clustering. Each round of position rearrangement will improve the overall order of objects, making it easier for users to use batch selection to select object groups. In addition, when the number of groups is larger, the time to complete the object grouping task in the embodiments of the present disclosure is more significantly increased compared with the existing methods. This is because when the number of groups is larger, the distribution of objects in the scene is more dispersed, and it is more difficult to use batch selection alone. In terms of the number of operations, the embodiments of the present disclosure include batch selection, and the number of operations is less than that of the method using single object selection. At the same time, due to the object order improved by position rearrangement, the efficiency of single batch selection is improved.

[0049] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for selecting a virtual scene object group based on probability propagation and position rearrangement, comprising: Construct a spatial grid to accelerate the probability propagation process, query the minimum side length of the collision body among all objects, and set it as the side length l of the smallest unit of the spatial grid min ; Pre-set the center position of the group to ensure that the positions between them are at the maximum value; Record a grouping probability vector P = (p1, p2, ..., p m ), where m is the number of groups, p i To predict the probability that the object belongs to the i-th group, the grouping probability vector of all objects is initially set to Detecting user selection operations on ungrouped objects; The probability propagation algorithm based on two strategies updates the grouping probability vector and determines the grouping priority of the remaining objects to be selected; An object position rearrangement algorithm based on grouping probability vector is used to relocate the position of the target object.

2. The method according to claim 1, wherein: The two strategies include: proximity exclusion and interval delivery, wherein, Proximity exclusion means that after users make batch selections, objects adjacent to the boundaries of the selected objects often belong to different groups, so the probability that these objects belong to the group to which the selected objects belong should be very low; Interval transfer is: for a grouped object, the probability that the group of ungrouped objects closest to it belongs to the same group as it decreases, and the probability that the group of ungrouped objects next to it belongs to the same group as it increases. At the same time, the influence of the grouped object on the adjacent ungrouped objects is limited by a distance threshold. The influence should decrease as the distance between the two increases. If the distance threshold is exceeded, the influence is reduced to 0.

3. The method according to claim 1, wherein: The probability propagation algorithm based on the two strategies updates the grouping probability vector and determines the grouping priority of the remaining objects to be selected, including: Set the grouping probability vector of the grouped objects to the standard unit vector e k , where k is the number of the group to which the object belongs; Create and maintain a queue similar to the one used in the breadth-first search algorithm, add all grouped objects to the queue, and for each group of objects in the same group, traverse each adjacent grid layer outward and add the ungrouped objects traversed to the queue; Starting from the first ungrouped object in the queue, update its grouping probability vector using the following formula: Where j = i-1, P i is the probability vector of grouping the i-th object in the queue, ⊙ is the Hadamard product, σ ij is the distance between the i-th object and the j-th object in the queue, is a vector of all 1s, and is obtained using the following formula: Among them, (x i ,y i , z i ) is the three-dimensional position coordinate of the i-th object in the virtual environment, and λ is a parameter defined by the user; The grouping probability vector of the object is enlarged, and the grouping probability vector of object i is Note γ=max(α1,α2,...,α m ), there is an amplification function: Among them, A() is the amplification function, β is the amplification coefficient, which is a variable parameter, and its value is taken as 0.8 here. is the grouping probability vector, γ is The infinite norm of The maximum value among the elements in .

4. The method according to claim 1, wherein: The object position rearrangement algorithm based on the grouping probability vector relocates the position of the target object, including: Set a cutoff threshold, set the probability components below the threshold to 0, and do not participate in the calculation of the new position of the object. For the grouping probability vector of object i There is a truncation function: in, is the i-th component of the new object grouping probability vector, med(α) is the median of each component of the grouping probability vector; The new position of the object is calculated by weighted mean of the grouping probability vector based on the object and the center position of each group. The new position of the object H(x, y, z) is obtained by the following formula: Among them, C i is the center position of each group, is the i-th component of the probability vector of the new grouping of objects; In response to the determined new position where there is an object, a breadth-first search algorithm is used with the position as the center to find the nearest empty space as the new position of the object.

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