Few-shot point cloud semantic segmentation method and apparatus, computer device, and storage medium

Abstract

A few-shot point cloud semantic segmentation method and apparatus, a computer device, and a storage medium. The method comprises: acquiring a support set point cloud and a query set point cloud corresponding to a mask, and performing feature extraction on the support set point cloud and the query set point cloud to obtain support set features and query set features; calculating the distance between the support set features and the query set features, and aligning the support set features on the basis of the distance to obtain the aligned support set features; acquiring corresponding prototypes on the basis of masks of the aligned support set features, splicing the prototypes and the query set features to obtain extended query set features, extracting point cloud feature distribution information from the extended query set features, and calibrating the point cloud feature distribution information; and segmenting calibrated point cloud feature distribution information to obtain calibrated prototypes and calibrated query set features. The present application can reduce the reasoning time and greatly reduce point cloud segmentation errors.

Description

Small sample point cloud semantic segmentation method, device, computer equipment and storage medium Technical Field

[0001] The present application belongs to the field of computer vision technology, and in particular relates to a method, apparatus, computer equipment, and storage medium for semantic segmentation of small sample point clouds. Background Art

[0002] The small-sample point cloud semantic segmentation method means that given a small number of labeled new category samples, the model can semantically segment the point cloud and obtain a point cloud belonging to the new category. In order to extract effective point cloud features, researchers are committed to improving the accuracy of prototypes. Multiple prototypes are redundant and take a lot of time in the reasoning process. Due to the complex structure of the point cloud, there is usually an inter-class distribution deviation and an intra-class distribution deviation between the ideal feature distribution and the actual feature distribution. Specifically, due to the distribution shift caused by the difference between the support data and the query data, there is an inter-class distribution deviation, and due to the different local structures of the classes, such as the seat and legs on a chair, the features of the query data may be scattered, which means that the query features are not closely around the relevant prototypes, resulting in intra-class distribution deviation. Due to the complex feature distribution of point clouds, how to reduce these deviations is a challenge.

[0003] In the existing technology, Snell et al. [J.Snell, K.Swersky, R.Zemel, Prototypical networks for few-shot learning, in: Advances in Neural Information Processing Systems (NeurIPS), 2017: 4080-4090.] proposed to use the average value of the class features as the prototype and segment the point cloud by the distance between the point features and the prototype. However, this method does not take into account the complexity of the structure of the point cloud itself, and the segmentation accuracy is not satisfactory. Zhao et al. [N.Zhao, T.-S.Chua, GHLee, Few-shot 3D point cloud semantic segmentation, in: IEEE / CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp.8869–8878.] designed a point cloud semantic segmentation method based on multiple prototypes, which uses multiple prototypes to describe the complex point cloud distribution, but its inference time is long.

[0004] Summary of the Invention

[0005] The present application provides a small sample point cloud semantic segmentation method, apparatus, computer equipment and storage medium, aiming to solve at least one of the above-mentioned technical problems in the prior art to a certain extent.

[0006] In order to solve the above problems, this application provides the following technical solutions:

[0007] A small sample point cloud semantic segmentation method, comprising:

[0008] Obtaining a support set point cloud and a query set point cloud corresponding to the mask, performing feature extraction on the support set point cloud and the query set point cloud to obtain support set features and query set features;

[0009] Calculating the distance between the support set features and the query set features, and aligning the support set features according to the distance to obtain aligned support set features;

[0010] A corresponding prototype is obtained according to the mask of the aligned support set features, the prototype is spliced ​​with the query set features to obtain an expanded query set feature, point cloud feature distribution information is extracted from the expanded query set features, and the point cloud feature distribution information is corrected to obtain corrected point cloud feature distribution information, and the corrected prototype and the corrected query set features are segmented from the corrected point cloud feature distribution information.

[0011] The technical solution adopted in the embodiment of the present application further includes: the distance between the support set features and the query set features is calculated specifically as follows:

[0012] In the above formula, F s represents the support set feature, F q represents the query set feature, i represents the support set feature F s The i-th feature in, j represents the query set feature F q The jth feature in d i,j Denotes the support set feature F s The i-th feature and the query set feature F q The distance of the jth feature in .

[0013] The technical solution adopted in the embodiment of the present application further includes: aligning the support set features according to the distance, and obtaining the aligned support set features specifically as follows:

[0014] For the support set feature F s With query set feature F q The distance is inverted and normalized to obtain the weight weight: weight i,j =softmax(-d i,j )

[0015] According to the weight, the feature that tends to be in the query set point cloud is obtained, and the feature is fused with the support set feature to obtain the aligned support set feature:

[0016] The technical solution adopted in the embodiment of the present application further includes: extracting point cloud feature distribution information from the expanded query set features, and correcting the point cloud feature distribution information, to obtain the corrected point cloud feature distribution information specifically as follows:

[0017] The point cloud feature distribution information F is extracted from the expanded query set features using a shared multi-layer perceptron e : F e =MLP(Con(F q ,P))

[0018] In the above formula, Con represents the concatenation operation;

[0019] The point cloud feature distribution information F e Multiply it with the transformed matrix to get the relationship matrix, and use the relationship matrix to calculate the point cloud feature distribution information F e Perform correction to obtain the corrected point cloud feature distribution information

[0020] The technical solution adopted in the embodiment of the present application further includes: the segmentation of the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information is specifically as follows:

[0021] In the above formula, and They represent the corrected prototype after segmentation and the corrected query set features respectively.

[0022] Another technical solution adopted in the embodiment of the present application is: a small sample point cloud semantic segmentation device, comprising:

[0023] Feature extraction module: used to obtain the support set point cloud and the query set point cloud corresponding to the mask, perform feature extraction on the support set point cloud and the query set point cloud to obtain support set features and query set features;

[0024] Feature alignment module: used to calculate the distance between the support set features and the query set features, align the support set features according to the distance, and obtain aligned support set features;

[0025] Distribution correction module: used to obtain the corresponding prototype according to the mask of the aligned support set features, splice the prototype with the query set features to obtain the expanded query set features, extract point cloud feature distribution information from the expanded query set features, and correct the point cloud feature distribution information to obtain the corrected point cloud feature distribution information, and segment the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information.

[0026] The technical solution adopted in the embodiment of the present application further includes: the feature alignment module calculates the distance between the support set features and the query set features, and aligns the support set features according to the distance as follows:

[0027] In the above formula, F s represents the support set feature, F q represents the query set feature, i represents the support set feature F s The i-th feature in, j represents the query set feature F q The jth feature in d i,j Denotes the support set feature F s The i-th feature and the query set feature F q The distance of the jth feature in;

[0028] For the support set feature F s With query set feature F q The distance is inverted and normalized to obtain the weight weight: weight i,j =softmax(-d i,j )

[0029] According to the weight, the feature that tends to be in the query set point cloud is obtained, and the feature is fused with the support set feature to obtain the aligned support set feature:

[0030] Among them, α is a hyperparameter used to maintain the original information of the feature.

[0031] The technical solution adopted in the embodiment of the present application further includes: the distribution correction module extracts point cloud feature distribution information from the expanded query set features, and corrects the point cloud feature distribution information to obtain corrected point cloud feature distribution information. The corrected prototype and the corrected query set features are segmented from the corrected point cloud feature distribution information. Specifically,

[0032] The point cloud feature distribution information F is extracted from the expanded query set features using a shared multi-layer perceptron e : F e =MLP(Con(F q,P))

[0033] In the above formula, Con represents the concatenation operation;

[0034] The point cloud feature distribution information F e Multiply it with the transformed matrix to get the relationship matrix, and use the relationship matrix to calculate the point cloud feature distribution information F e Perform correction to obtain the corrected point cloud feature distribution information

[0035] Segment the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information:

[0036] In the above formula, and Represent the segmented corrected prototype and the corrected query set features, respectively. and It represents the final prototype and query set features, and β is a hyperparameter used to maintain the original information of the features.

[0037] Another technical solution adopted by the embodiment of the present application is: a computer device, the computer device comprising a processor and a memory coupled to the processor, wherein:

[0038] The memory stores program instructions for implementing the small sample point cloud semantic segmentation method;

[0039] The processor is used to execute the program instructions stored in the memory to control a small sample point cloud semantic segmentation method.

[0040] Another technical solution adopted in the embodiment of the present application is: a storage medium storing program instructions executable by a processor, wherein the program instructions are used to execute the small sample point cloud semantic segmentation method.

[0041] Compared with the prior art, the beneficial effects produced by the embodiments of the present application are as follows: the small sample point cloud semantic segmentation method, device, computer equipment and storage medium of the embodiments of the present application perform feature extraction on the support set point cloud and the query set point cloud through a feature extractor to obtain support set features and query set features, and use a feature alignment module to align the support set features and the query set features, thereby reducing the inter-class distribution deviation caused by the difference between the support set point cloud and the query set point cloud, and use a distribution correction module to adjust the feature distribution information of the prototype and query set features, thereby reducing the intra-class distribution deviation caused by the difference in feature distribution between the prototype and the query set features. While reducing the inference time, the point cloud segmentation error is greatly reduced, and the accuracy of the semantic segmentation of small sample point clouds is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG1 is a flowchart of a small sample point cloud semantic segmentation method according to an embodiment of the present application;

[0043] FIG2 is a schematic diagram of a framework of a small sample point cloud semantic segmentation system according to an embodiment of the present application;

[0044] FIG3 is a schematic diagram of a feature alignment operation of a feature alignment module in an embodiment of the present application;

[0045] FIG4 is a schematic diagram of characteristic correction of a distribution correction module in an embodiment of the present application;

[0046] FIG5 is a schematic diagram of the structure of a small sample point cloud semantic segmentation device according to an embodiment of the present application;

[0047] FIG6 is a schematic diagram of the structure of a computer device according to an embodiment of the present application;

[0048] FIG7 is a schematic diagram of the structure of a storage medium according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] The terms "first," "second," and "third" in this application are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of such features. In the description of this application, "multiple" means at least two, for example, two, three, etc., unless otherwise specifically defined. All directional indications in the embodiments of this application (such as up, down, left, right, front, back...) are only used to explain the relative positional relationship, movement, etc. between the components under a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications also change accordingly. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or computer device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products, or computer devices.

[0051] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0052] Please refer to Figures 1 and 2. Figure 1 is a flow chart of the small sample point cloud semantic segmentation method according to an embodiment of the present application, and Figure 2 is a schematic diagram of the framework of the small sample point cloud semantic segmentation system according to an embodiment of the present application. Specifically, the small sample point cloud semantic segmentation method according to an embodiment of the present application includes the following steps:

[0053] S100: Obtain a support set point cloud and a query set point cloud corresponding to the mask, and perform feature extraction on the support set point cloud and the query set point cloud through a feature extractor to obtain support set features and query set features;

[0054] S110: Calculating the distance between the support set features and the query set features through a feature alignment module, and aligning the support set features according to the distance to obtain aligned support set features;

[0055] In this step, as shown in FIG3 , a schematic diagram of the feature alignment operation of the feature alignment module in an embodiment of the present application is shown. Specifically, the feature alignment algorithm of the feature alignment module includes:

[0056] Step 1: Calculate the support set feature F s With query set feature F q Distance:

[0057] In the above formula, F s represents the support set feature, F q represents the query set feature, i represents the support set feature F s The i-th feature in, j represents the query set feature F q The jth feature in d i,j Denotes the support set feature F s The i-th feature and the query set feature F q The distance of the jth feature in .

[0058] Step 2: For the support set feature F s With query set feature F q The distance is inverted and normalized to obtain the weight weight: weight i,j =softmax(-d i,j ) (2)

[0059] The third step is to obtain the features that tend to be in the query set point cloud according to the weight, and fuse the features with the support set features to obtain the aligned support set features:

[0060] Among them, α is a hyperparameter used to maintain the original information of the feature.

[0061] It can be understood that the embodiment of the present application uses the feature alignment module to align the support set features and the query set features in the first stage, thereby reducing the inter-class distribution deviation caused by the difference between the support set point cloud and the query set point cloud.

[0062] S120: Obtaining the corresponding prototype according to the mask of the aligned support set features, and splicing the prototype with the query set features using a distribution correction module to obtain an expanded query set feature, extracting point cloud feature distribution information from the expanded query set feature, and correcting the point cloud feature distribution information to obtain corrected point cloud feature distribution information;

[0063] In this step, please refer to FIG4 , which is a schematic diagram of the feature correction of the distribution correction module in the embodiment of the present application. Specifically, the feature correction algorithm of the distribution correction module includes:

[0064] S121: Combine prototype P with query set feature F q Perform splicing to obtain the expanded query set features;

[0065] S122: Use a shared multi-layer perceptron (MLP) to extract point cloud feature distribution information F from the expanded query set features e : F e =MLP(Con(F q ,P)) (4)

[0066] In formula (4), Con represents the concatenation operation.

[0067] S123: Point cloud feature distribution information F e Multiply it with the transformed matrix to get the relationship matrix, and use the relationship matrix to calculate the point cloud feature distribution information F e Perform correction to obtain the corrected point cloud feature distribution information

[0068] S130: Segmenting a corrected prototype and a corrected query set feature from the corrected point cloud feature distribution information through a distribution correction module;

[0069] In this step, in order to ensure the original feature information, the distribution correction module adds the original feature information to the corrected prototype and query set features during segmentation. The segmentation formula is as follows:

[0070] In the above formula, and Represent the segmented corrected prototype and the corrected query set features, respectively. and It represents the final prototype and query set features, and β is a hyperparameter used to maintain the original information of the features.

[0071] It can be understood that the embodiment of the present application uses the distribution correction module to adjust the feature distribution information in the second stage, which can reduce the intra-class deviation and obtain a better segmentation effect.

[0072] Based on the above, the small sample point cloud semantic segmentation method of the embodiment of the present application uses a feature extractor to extract features from the support set point cloud and the query set point cloud to obtain support set features and query set features, and uses a feature alignment module to align the support set features and the query set features, thereby reducing the inter-class distribution deviation caused by the difference between the support set point cloud and the query set point cloud, and uses a distribution correction module to adjust the feature distribution information of the prototype and query set features, thereby reducing the intra-class distribution deviation caused by the difference in feature distribution between the prototype and the query set features. While reducing the inference time, it greatly reduces the point cloud segmentation error and improves the accuracy of small sample point cloud semantic segmentation.

[0073] Please refer to FIG5 , which is a schematic diagram of the structure of a small sample point cloud semantic segmentation device according to an embodiment of the present application. The small sample point cloud semantic segmentation device 40 according to the embodiment of the present application includes:

[0074] Feature extraction module 41: used to obtain a support set point cloud and a query set point cloud corresponding to the mask, perform feature extraction on the support set point cloud and the query set point cloud to obtain support set features and query set features;

[0075] Feature alignment module 42: used to calculate the distance between the support set features and the query set features, align the support set features according to the distance, and obtain aligned support set features;

[0076] Distribution correction module 43: used to obtain the corresponding prototype according to the mask of the aligned support set features, splice the prototype with the query set features to obtain the expanded query set features, extract point cloud feature distribution information from the expanded query set features, and correct the point cloud feature distribution information to obtain the corrected point cloud feature distribution information, and segment the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information.

[0077] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0078] The device provided in the embodiment of the present application can be applied in the aforementioned method embodiment. For details, please refer to the description of the aforementioned method embodiment, which will not be repeated here.

[0079] Please refer to FIG6 , which is a schematic diagram of the computer device structure of an embodiment of the present application. The computer device 50 includes:

[0080] A memory 51 storing executable program instructions;

[0081] a processor 52 connected to the memory 51;

[0082] The processor 52 is used to call the executable program instructions stored in the memory 51 and execute the following steps: obtain the support set point cloud and the query set point cloud of the corresponding mask, perform feature extraction on the support set point cloud and the query set point cloud to obtain support set features and query set features; calculate the distance between the support set features and the query set features, align the support set features according to the distance, and obtain aligned support set features; obtain the corresponding prototype according to the mask of the aligned support set features, splice the prototype with the query set features to obtain expanded query set features, extract point cloud feature distribution information from the expanded query set features, and correct the point cloud feature distribution information to obtain corrected point cloud feature distribution information, and segment the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information.

[0083] The processor 52 may also be referred to as a CPU (Central Processing Unit). The processor 52 may be an integrated circuit chip having signal processing capabilities. The processor 52 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor or any conventional processor.

[0084] Please refer to Figure 7, which is a schematic diagram of the structure of the storage medium of an embodiment of the present application. The storage medium of the embodiment of the present application stores program instructions 61 that can implement the following steps: obtaining a support set point cloud and a query set point cloud corresponding to a mask, performing feature extraction on the support set point cloud and the query set point cloud to obtain support set features and query set features; calculating the distance between the support set features and the query set features, aligning the support set features according to the distance, and obtaining aligned support set features; obtaining the corresponding prototype according to the mask of the aligned support set features, splicing the prototype with the query set features to obtain an expanded query set feature, extracting point cloud feature distribution information from the expanded query set feature, and correcting the point cloud feature distribution information to obtain corrected point cloud feature distribution information, and segmenting the corrected prototype and corrected query set features from the corrected point cloud feature distribution information. The program instructions 61 can be stored in the above-mentioned storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, server, or network computer device, etc.) or a processor to execute all or part of the steps of the method of each embodiment of the present application. The aforementioned storage media include: various media that can store program instructions, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks, or terminal computer devices such as computers, servers, mobile phones, and tablets. The server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0086] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. The above is only an implementation method of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of this application, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A small sample point cloud semantic segmentation method, characterized in that: include: Obtaining a support set point cloud and a query set point cloud corresponding to the mask, performing feature extraction on the support set point cloud and the query set point cloud to obtain support set features and query set features; Calculating the distance between the support set features and the query set features, aligning the support set features according to the distance, and obtaining aligned support set features; A corresponding prototype is obtained according to the mask of the aligned support set features, the prototype is spliced ​​with the query set features to obtain an expanded query set feature, point cloud feature distribution information is extracted from the expanded query set features, and the point cloud feature distribution information is corrected to obtain corrected point cloud feature distribution information, and the corrected prototype and the corrected query set features are segmented from the corrected point cloud feature distribution information.

2. The small sample point cloud semantic segmentation method according to claim 1, characterized in that: The calculation of the distance between the support set feature and the query set feature is specifically as follows: In the above formula, F s represents the support set feature, F q represents the query set feature, i represents the support set feature F s The i-th feature in, j represents the query set feature F q The jth feature in d i,j Denotes the support set feature F s The i-th feature in the query set F q The distance of the jth feature in .

3. The small sample point cloud semantic segmentation method according to claim 2, characterized in that: The support set features are aligned according to the distance to obtain the aligned support set features: For the support set feature F s With the query set feature F q The distance is inverted and normalized to obtain the weight weight: weight i,j =softmax(-d i,j ) According to the weight, the feature that tends to be in the query set point cloud is obtained, and the feature is fused with the support set feature to obtain the aligned support set feature: Among them, α is a hyperparameter used to maintain the original information of the feature.

4. The small sample point cloud semantic segmentation method according to claim 3, characterized in that: The step of extracting point cloud feature distribution information from the expanded query set features and correcting the point cloud feature distribution information to obtain the corrected point cloud feature distribution information is specifically: The point cloud feature distribution information F is extracted from the expanded query set features using a shared multi-layer perceptron e : F e =MLP(Con(F q ,P)) In the above formula, Con represents the concatenation operation; The point cloud feature distribution information F e Multiply it with the transformed matrix to get the relationship matrix, and use the relationship matrix to calculate the point cloud feature distribution information F e Perform correction to obtain the corrected point cloud feature distribution information 5. The small sample point cloud semantic segmentation method according to claim 4, characterized in that: The step of segmenting the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information is as follows: In the above formula, and Respectively represent the corrected prototype of the segmentation and the corrected query set features, and represents the final prototype and query set features, and β is a hyperparameter used to maintain the original information of the features.

6. A small sample point cloud semantic segmentation device, characterized in that: include: Feature extraction module: used to obtain the support set point cloud and the query set point cloud corresponding to the mask, perform feature extraction on the support set point cloud and the query set point cloud, and obtain support set features and query set features; Feature alignment module: used to calculate the distance between the support set features and the query set features, align the support set features according to the distance, and obtain aligned support set features; Distribution correction module: used to obtain the corresponding prototype according to the mask of the aligned support set features, splice the prototype with the query set features to obtain the expanded query set features, extract the point cloud feature distribution information from the expanded query set features, and correct the point cloud feature distribution information to obtain the corrected point cloud feature distribution information, and segment the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information.

7. The small sample point cloud semantic segmentation device according to claim 6, characterized in that: The feature alignment module calculates the distance between the support set feature and the query set feature, and aligns the support set features according to the distance as follows: In the above formula, F s represents the support set feature, F q represents the query set feature, i represents the support set feature F s The i-th feature in, j represents the query set feature F q The jth feature in d i,j Denotes the support set feature F s The i-th feature in the query set F q The distance of the jth feature in; For the support set feature F s With the query set feature F q The distance is inverted and normalized to obtain the weight weight: weight i,j =softmax(-d i,j ) According to the weight, the feature that tends to be in the query set point cloud is obtained, and the feature is fused with the support set feature to obtain the aligned support set feature:

8. The small sample point cloud semantic segmentation device according to claim 7, characterized in that: The distribution correction module extracts point cloud feature distribution information from the expanded query set features, and corrects the point cloud feature distribution information to obtain corrected point cloud feature distribution information, and segments the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information as follows: The point cloud feature distribution information F is extracted from the expanded query set features using a shared multi-layer perceptron e : F e =MLP(Con(F q ,P)) In the above formula, Con represents the concatenation operation; The point cloud feature distribution information F e Multiply it with the transformed matrix to get the relationship matrix, and use the relationship matrix to calculate the point cloud feature distribution information F e Perform correction to obtain the corrected point cloud feature distribution information Segment the corrected prototype and the corrected query set features from the corrected point cloud feature distribution information: In the above formula, and They represent the corrected prototype after segmentation and the corrected query set features respectively.

9. A computer device, characterized in that: The computer device includes a processor and a memory coupled to the processor, wherein: The memory stores program instructions for implementing the small sample point cloud semantic segmentation method according to any one of claims 1 to 5; The processor is used to execute the program instructions stored in the memory to control a small sample point cloud semantic segmentation method.

10. A storage medium, characterized in that: The device stores program instructions executable by a processor, wherein the program instructions are used to execute the small sample point cloud semantic segmentation method according to any one of claims 1 to 5.

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