Methods, apparatuses, devices, media, and products for processing anchor points

Abstract

Embodiments of the present disclosure relate to a method, device, equipment, medium and product for processing anchor points. The method comprises receiving, at a server, a set of images for a target scene from a terminal. The method further comprises determining a point cloud for the target scene and a relative pose of a target anchor point in the target scene relative to the point cloud. The method further comprises determining a target pose of the target anchor point in the terminal based on the set of images, the point cloud and the relative pose. Through the method, the pose of the anchor point in the terminal can be quickly and accurately determined by using the set of images and the point cloud and the pose of the anchor point relative to the point cloud, so that the anchor point can be quickly located in the terminal device, thereby realizing sharing of the anchor point between devices and improving user experience.

Description

Technical Field

[0001] The embodiments of this disclosure generally relate to the field of spatial positioning technology, and more specifically to methods, apparatus, devices, media, and products for processing anchor points. Background Technology

[0002] With the development of spatial computing and immersive interaction technologies, spatial collaboration capabilities across multiple devices and platforms are gradually becoming a crucial foundation for smart terminal scenarios. Achieving consistent spatial understanding and information sharing across different terminals in areas such as multi-user collaboration, distance education, and social entertainment brings users a more immersive and consistent interactive experience. Especially against the backdrop of the rapid expansion of extended reality technology applications, spatial anchors are playing an increasingly central role in connecting real and virtual information.

[0003] Currently, with the enrichment of the content ecosystem and the widespread adoption of smart devices, the demand for spatial synchronization and anchor point sharing is showing a diversified trend. Various terminals are continuously expanding their capabilities in virtual interaction, spatial presentation, and information sharing, driving the evolution of spatial content from single devices to multi-terminal, multi-platform collaborative development. As scenario applications continue to deepen, this capability is gradually becoming an important infrastructure supporting multi-role, multi-space interactive experiences, demonstrating broad development prospects and application value. Summary of the Invention

[0004] Embodiments of this disclosure provide a method, apparatus, device, medium, and product for processing anchor points.

[0005] According to a first aspect of this disclosure, a method for processing anchor points is provided. The method includes receiving a set of images of a target scene from a terminal at a server. The method further includes determining a point cloud of the target scene and the relative pose of a target anchor point in the target scene relative to the point cloud. The method also includes determining a target pose of the target anchor point on the terminal based on the set of images, the point cloud, and the relative pose.

[0006] According to a second aspect of this disclosure, a method for processing anchor points is provided. The method includes receiving, at a terminal, a request for sharing a target anchor point in a target scene. The method further includes acquiring, based on the request, a set of images for the target scene. The method further includes sending the set of images to a server. The method further includes receiving, based on the transmission of the set of images, a target pose of the target anchor point in the terminal from the server, the target pose being determined based on the set of images, a point cloud of the target scene, and the relative pose of the target anchor point relative to the point cloud.

[0007] According to a third aspect of this disclosure, an apparatus for processing anchor points is provided. The apparatus includes an image receiving module configured to receive a set of images of a target scene from a terminal at a server; a point cloud and anchor point relative pose determination module configured to determine the relative pose of the point cloud of the target scene and a target anchor point in the target scene relative to the point cloud; and a target pose determination module configured to determine the target pose of the target anchor point on the terminal based on the set of images, the point cloud, and the relative pose.

[0008] A fourth aspect of this disclosure provides an apparatus for processing anchor points. The apparatus includes: a request receiving module configured to receive at a terminal a request for sharing a target anchor point in a target scene; an image acquisition module configured to acquire a set of images of the target scene; an image sending module configured to send the set of images to a server; and a target pose receiving module configured to receive, based on the transmission of the set of images, a target pose of the target anchor point in the terminal from the server, the target pose being determined based on the set of images, a point cloud of the target scene, and the relative pose of the target anchor point relative to the point cloud.

[0009] In a fifth aspect of this disclosure, an electronic device is provided, including at least one processor; and a storage device for storing at least one program, which, when executed by the at least one processor, causes the at least one processor to implement the methods according to the first and second aspects of this disclosure.

[0010] In a sixth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods according to the first and second aspects of this disclosure.

[0011] In a seventh aspect of this disclosure, a computer program product is provided. This computer program product includes a computer program that, when executed by a processor, implements the methods according to the first and second aspects of this disclosure.

[0012] It should be understood that the content described in this section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0014] Figure 1The illustration shows a schematic diagram of an example environment in which some embodiments of the present disclosure may be implemented;

[0015] Figure 2 The illustration shows a schematic diagram of an example method for processing anchor points according to some embodiments of the present disclosure;

[0016] Figure 3 The illustration shows a schematic diagram of another example method for processing anchor points according to some embodiments of the present disclosure;

[0017] Figure 4 The illustration shows a flowchart of an example process for cross-vendor device mapping according to some embodiments of the present disclosure;

[0018] Figure 5 The illustration shows a flowchart of an example process for cross-vendor device positioning according to some embodiments of the present disclosure;

[0019] Figure 6 The illustration shows an example process of an X vendor device sharing an anchor point to a Y vendor device according to some embodiments of the present disclosure;

[0020] Figure 7 The illustration shows an example process of a Y vendor device sharing an anchor point to an X vendor device according to some embodiments of the present disclosure;

[0021] Figure 8 The illustration shows a schematic block diagram of an apparatus for processing anchor points according to some embodiments of the present disclosure;

[0022] Figure 9 The illustration shows a schematic block diagram of another apparatus for processing anchor points according to some embodiments of the present disclosure;

[0023] Figure 10 A schematic block diagram of an example device suitable for implementing various embodiments of the present disclosure is illustrated. Detailed Implementation

[0024] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0026] For example, upon receiving a user's proactive request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0027] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0028] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0029] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0030] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0031] As mentioned above, with the continuous development of spatial computing, smart devices, and extended reality technologies, applications based on spatial perception are expanding into multiple fields such as cultural tourism guidance, immersive entertainment, and remote collaboration. The need for multiple terminals and platforms to work collaboratively in the same physical environment is gradually emerging, especially in applications requiring spatial consistency, which places higher demands on the integration of virtual and reality. Currently, in scenarios such as multi-person interaction, real-time positioning, and information sharing, how different devices can accurately perceive and understand the same spatial object has become a crucial foundation for promoting the widespread adoption of related applications and improving user experience. The continuous improvement of image processing, sensor performance, and cloud computing capabilities provides more reliable technical support for multi-terminal spatial synchronization and lays a solid foundation for the subsequent development of spatial collaboration.

[0032] In existing multi-terminal spatial collaboration applications, although the perception capabilities and environmental modeling accuracy of terminal devices continue to improve, and the performance of devices in spatial recognition and content presentation is constantly being optimized, certain coordination difficulties still exist when collaboration between devices from different platforms or manufacturers is involved. When different terminals identify or reproduce the same physical space, inconsistencies in the location of virtual content may occur, affecting the consistency and immersion of users during cross-device interaction, thus interfering with the overall collaborative experience. For example, in multi-device collaboration or immersive interaction scenarios, users typically use multiple terminal devices to perceive or present the same real-world scene. For existing Extended Reality (XR) devices, because different device providers usually use different types of image sensors, there may be issues with their own independent world coordinate system definitions or incompatible data formats, making it impossible to share and recognize anchor point information between devices. An anchor point refers to a specific point or plane in the real-world physical space that is associated with a virtual object. It is used to determine the position and orientation of the virtual object in the real world, playing a role in fixing and tracking virtual content, enabling the virtual object to maintain a consistent spatial relationship with the real environment.

[0033] To address this, embodiments of this disclosure propose a method for processing anchor points. In this method, a server receives a set of images for a target scene from a terminal device. The server determines a point cloud for the target scene and the relative pose of a target anchor point within the target scene relative to the point cloud. Finally, the server determines the target pose of the target anchor point on the terminal based on the set of images, the point cloud, and the relative pose. This method allows the anchor point's pose on the terminal to be determined using the point cloud and its relative pose, thereby enabling cross-device anchor point sharing and alignment, improving the stability and consistency of multi-device spatial collaboration, and enhancing the user experience.

[0034] The embodiments of this disclosure will now be described in further detail with reference to the accompanying drawings. Figure 1 The illustration shows an example environment in which the devices and / or methods of embodiments of this disclosure can be implemented. In environment 100, terminal 102 and server 104 can achieve the ability to share anchors across platforms. For ease of description, terminal 102 may also be referred to as a first terminal in the following description.

[0035] Examples of terminal 102 include, but are not limited to, augmented reality devices, virtual reality devices, extended reality devices, head-mounted display devices, mobile devices (e.g., smartphones), laptop computers, portable digital assistants (PDAs), e-book readers, portable media players, game consoles, set-top boxes (STBs), smart TVs (TVs), personal computers, in-vehicle computers (e.g., navigation units), etc. Examples of server 104 include, but are not limited to, server computers, multiprocessor systems, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0036] To achieve consistent identification of spatial anchor points across devices, a user can use terminal 102 to acquire a set of images 106 in a target scene. Anchor points may be set in this target scene, such as target anchor points set by other users' devices. Then, when locating the target anchor point, terminal 102 can send the set of images 106 in the target scene to server 104 to obtain the target pose 112 of the anchor point from server 104. For ease of description, this set of images 106 can also be referred to as the first set of images, for example, it includes image frames or video frames acquired by the camera of terminal 102 over a continuous period of time.

[0037] like Figure 1 As shown, the server 104 stores a point cloud 108 for the target scene and a relative pose 110 of the target anchor point in the target scene. This relative pose is the relative pose of the target anchor point with respect to the point cloud 108. The point cloud of the target scene is generated by image information received from other terminals, such as a set of images received from a second terminal. These other terminals and terminal 102 can be devices provided by different manufacturers. For example, the types of sensors used to acquire images in the other terminals and terminal 102 are different, which makes the coordinate systems or data transmission standards or protocols of the different terminals different.

[0038] For example, when generating the point cloud, server 104 can obtain a second set of images from the second terminal, and can further obtain the identifier and / or pose of the target anchor point set in the second terminal. Additionally, the second terminal can further calculate the relative pose of the target anchor point with respect to the images in the second set of images, for example, calculating the relative pose of the target anchor point with respect to each image in the second set of images. Furthermore, server 104 can also obtain the provider or vendor identifier of the second terminal from the second terminal. Then, server 104 can use the second set of images and the relative pose of the target anchor point with respect to the images in the second set of images to generate point cloud 108 and relative pose 110 of the target anchor point with respect to the point cloud.

[0039] After receiving a set of images 106 uploaded by terminal 102, server 104 first performs feature point extraction on the images, extracting key feature points such as corner points, edge points, or depth features. Then, server 102 matches the key feature points in the set of images with the point cloud to determine the transformation relationship between the images in the set of images 106 and the point cloud 108. This matching process can be completed through feature point similarity calculation and spatial consistency calculation, thereby ensuring the accuracy and stability of the matching results. Based on the final matching results, server 104 can determine the spatial mapping matrix between the images in the set of images and the 3D point cloud. This matrix allows the determination of the transformation relationship between the origin coordinates of the terminal and the point cloud coordinates. Then, the relative pose of the target anchor point is used in conjunction with this transformation relationship to determine the relative pose of the target anchor point relative to the set of images 106.

[0040] Finally, the server 102 can determine the target pose 114 of the anchor point in the second terminal based on the relative pose of the target anchor point for each image in a set of images, and after optimizing the accuracy, send the target pose to the terminal 102 to realize the restoration of the anchor point pose.

[0041] In some embodiments, before sending a set of images to the server 104, the terminal 102 can receive a sharing request for a target anchor point from a second terminal. In one example, after receiving the sharing request, the terminal 102 sends the anchor point identifier of the target anchor point in the sharing request to the server to determine whether the target anchor point is provided by a different device provider or vendor. If it is provided by a different device provider or vendor, the server 104 instructs the terminal 102 to upload a set of images. If it is provided by the same device provider, the server 104 can directly return the target pose of the target anchor point to the terminal. In another example, after receiving the request, the terminal 102 first verifies the identity of the device provider or vendor identifier of the second terminal contained in the request. For example, the terminal 102 determines whether the device provider of the second terminal is the same as the device provider of the terminal based on a preset provider list. When it is confirmed that the device providers are different, the terminal 102 enters a determination preparation process. In this process, the terminal 102 acquires a set of images 106. For example, the terminal collects images of the target scene, such as continuously acquiring multiple frames of image data over a period of time. These image data can cover multiple perspectives of the user's current spatial environment to improve the accuracy of subsequent 3D reconstruction and anchor point matching. After acquisition, terminal 102 will upload the set of images to server 104.

[0042] In some embodiments, before issuing a share anchor point request, the second terminal needs to complete the creation process of the target anchor point. During this process, the user can specify a target anchor point for a target scene through the second terminal, using methods such as clicking, coordinate input, intelligent recognition, or external device-assisted point selection. To this end, the second terminal will collect a second set of images within a specified time period in the user-specified scene, for example, extracting a continuous set of images. Additionally, the second terminal, combining its own camera parameters, calculates the relative pose of the target anchor point with respect to the images in this set of images. Subsequently, the second terminal sends the relative pose information of the target anchor point relative to the images, the anchor point identifier, and the second set of images together to the server 104.

[0043] After receiving the aforementioned information uploaded by the terminal, the server 104 can execute the 3D reconstruction process based on the second set of images. This process mainly includes two steps: first, generating a point cloud 108 of the target scene to reconstruct the spatial structure of the terminal's acquisition environment; second, determining the relative pose of the target anchor point relative to the point cloud. The server performs camera pose estimation on each frame in the set of images to obtain the spatial position and orientation of the corresponding image frame in a unified coordinate system. During the 3D reconstruction process, the server can extract multiple feature points (such as corner points, edge points, or depth features) from the keyframe images and use visual algorithms to construct dense or sparse 3D point cloud data. Subsequently, the server 104 further performs feature matching between the extracted image features and the constructed point cloud to establish the correspondence between the image frames and the point cloud. Based on these correspondences, the server can calculate the spatial mapping matrix between the image frames and the 3D point cloud, that is, the transformation parameters (usually a combination of rotation matrix and translation vector) from the image coordinate system to the point cloud coordinate system. Through this mapping matrix, the server can not only achieve accurate alignment between anchor points in the image and 3D point clouds in the point cloud, but also further determine the relative pose of the target anchor point relative to the point cloud. In addition, the server will also establish a mapping relationship between the target anchor point, the device provider or manufacturer of the terminal that created the anchor point, the point cloud, and the relative pose of the target anchor point relative to point cloud 108, as the basic data support for subsequent implementation of anchor point sharing, cross-terminal synchronization, or spatial content interaction.

[0044] In some embodiments, the second terminal may receive a request from terminal 102 to share the anchor point, so as to accurately restore the position of the anchor point on the second terminal. For example, in applications such as virtual furniture arrangement, remote collaboration, or augmented reality navigation, users may want to find a target anchor point at a key location in a real physical space and want that anchor point to be correctly displayed on the second terminal device.

[0045] Additionally, the first terminal and the second terminal are devices from different equipment providers or manufacturers, while the server may be a device provided by the same service provider or manufacturer as the first terminal or the second terminal.

[0046] This method, through collaboration between the terminal and the server, combined with the relative pose of the anchor point to the point cloud, further optimizes the spatial positioning accuracy and consistency of the anchor point across multiple terminals. It effectively improves the repositioning accuracy of the anchor point in different device coordinate systems and significantly reduces the risk of spatial drift or inconsistent position of the anchor point. Thus, in scenarios such as multi-terminal collaboration and immersive display, it enhances the spatial alignment effect between virtual content and the physical environment and improves the user experience.

[0047] The above combination Figure 1 The following is a schematic diagram illustrating an example environment in which some embodiments of this disclosure may be implemented, in conjunction with... Figure 2 A schematic diagram illustrating an example method for processing anchor points according to some embodiments of the present disclosure. Figure 2 The method in can be derived from Figure 1 It can be executed on server 104 or any suitable computing device.

[0048] like Figure 2 In example method 200, at block 202, a set of images for the target scene is received from terminal 102 at server 104. This set of images may include a sequence of image frames continuously acquired by the terminal's image acquisition module over a period of time during the user's initiation of an anchor point sharing request. This set of images includes not only visual information but may also include data such as timestamps of image acquisition times, terminal pose information, image resolution, and image sensor parameters. This data can be used for subsequent processing on the server side, such as anchor point localization, feature matching, and camera pose estimation.

[0049] In some embodiments, the target scene can refer to the real three-dimensional space in which the user is currently located, such as a physical environment like a conference room or living room. To ensure the spatial continuity of the images and the accuracy of anchor point reconstruction, the acquired images are usually multiple consecutive frames within a predetermined time window, such as a sequence of 60 images acquired within 2 seconds at a rate of 30 frames per second.

[0050] In some embodiments, server 104 may also receive a target anchor point identifier from terminal 104. Then, server 104 may store a mapping relationship between the target anchor point identifier, point cloud, the identifier of the device provider of the second device, and the relative pose of the target anchor point relative to the point cloud. Server 104 further determines whether the target anchor point identifier exists in the mapping relationship. If the target anchor point identifier exists in the mapping relationship, server 104 sends a request to first terminal 102 to acquire a first set of images.

[0051] At frame 204, server 104 determines the point cloud of the target scene and the relative pose of the target anchor point in the target scene relative to the point cloud. After receiving a set of images from terminal 102, server 104 also needs to further determine the point cloud 108 corresponding to the target scene and the relative pose of the target anchor point relative to the point cloud. The point cloud 108 and the relative pose of the target anchor point relative to the point cloud on the server can be predetermined.

[0052] For example, server 104 can receive a second set of images of the target scene and anchor point information of the target anchor point from the second terminal. The anchor point information includes the reference relative pose of the target anchor point relative to each image in the second set of images. The server performs a 3D reconstruction process based on the second set of images to generate a 3D point cloud corresponding to the target scene. The 3D point cloud is a set of spatial points calculated using algorithms such as image matching, feature extraction and tracking, and multi-view geometric reconstruction. This set can represent the spatial structure of the object surface in the target scene. For example, the server can determine the camera pose for each image in the second set of images by matching feature points in the second set of images. Then, the server uses the camera pose and the matched feature points in the second set of images to generate the point cloud. After generating the point cloud, the server further uses the point cloud and the reference relative pose to determine the relative pose of the target anchor point relative to the point cloud. For example, the server can further identify the relative pose of the target anchor point with respect to the corresponding image keyframe. By using the mapping matrix calculated between the image and the point cloud, the target anchor point can be mapped from the image to the three-dimensional point cloud space, thereby determining the three-dimensional spatial pose of the target anchor point relative to the point cloud, including the three-dimensional coordinates and orientation, i.e., the relative pose.

[0053] In some embodiments, the server 104 may further determine the mapping relationship between the target anchor point identifier, the point cloud, and the relative pose of the target anchor point relative to the point cloud. Additionally, the mapping relationship may also include the identifier of the device provider or manufacturer of the second device.

[0054] At frame 206, server 104 determines the target pose of the target anchor point on the terminal based on a set of images, point clouds, and relative poses. After obtaining a set of images 106, server 104 can combine the point cloud and the relative pose of the target anchor point in the point cloud space to determine the target pose of the target anchor point on the terminal 102.

[0055] In this process, server 104 uses the set of images 106 and the point cloud to match and determine the transformation relationship between the images and the point cloud in the set of images. For example, it can determine the mapping matrix between the image coordinate system and the point cloud coordinate system. Then, based on this transformation relationship and the relative pose of the target anchor point in the point cloud space, it transforms the pose of the anchor point in the current coordinate system of the terminal, i.e., the target pose. This target pose can be used to render virtual objects or establish shared anchor points on the terminal device, and can be used in real time for multi-user shared scene alignment.

[0056] In some embodiments, the server 104 can jointly optimize the projection accuracy of the anchor points using images and point clouds. After the terminal uploads the image, the target pose of the anchor points can be obtained through preliminary registration. When image blurring occurs, the server can introduce multiple viewpoint images to assist in optimizing the registration accuracy of the point cloud, ultimately generating a more accurate anchor point pose from the terminal's perspective, enabling precise alignment and display of the anchor points in the three-dimensional virtual space.

[0057] This method integrates image data, 3D point cloud, and anchor point relative pose into the anchor point positioning process, making the server's spatial recovery of anchor points on the terminal more accurate and reliable, and effectively improving cross-device anchor point synchronization and user experience in multi-scenario collaboration.

[0058] The above combination Figure 2 The following is a schematic diagram illustrating an example method for processing anchor points according to some embodiments of the present disclosure, in conjunction with... Figure 3 A schematic diagram illustrating another example method for processing anchor points according to some embodiments of the present disclosure. Figure 3 Example method 300 in the example can be derived from Figure 1 The terminal 102 or any suitable computing device in the system can be used for processing.

[0059] like Figure 3 In example method 300, at block 302, a request for sharing target anchor points in a target scene is received at terminal 102. In some embodiments, this request may be initiated by other devices and sent to the terminal via a network for collaborative operation. The request content typically includes the anchor point identifier, the scene identifier, and the device information of the request initiating the request, so that the terminal can determine its positioning capabilities and prepare for image acquisition and positioning processing.

[0060] At box 304, terminal 102 retrieves a set of images for a target scene based on a request. Upon receiving the request, terminal 102 needs to further determine whether the target anchor point is set by a different type of device. In one example, terminal 102 sends the anchor point identifier for the target anchor point to the server to determine whether the anchor point is provided by another device provider. For example, it searches in the server's storage of a mapping relationship between anchor points and device providers. If found in the mapping relationship, the server sends an instruction to the terminal to retrieve a set of images for the target scene. In another example, after receiving the request, terminal 102 performs a local search based on the anchor point identifier. If not found, it sends the anchor point identifier to the server to further determine whether it is provided by a device provider different from the terminal. In yet another example, terminal 102 can determine locally on the device whether the anchor point identifier is determined by a device from a different device provider.

[0061] Additionally, when a set of images needs to be acquired, terminal 102 determines whether the current environment is the target scene based on the target scene information in the request, such as the scene identifier. After confirmation, it initiates the image acquisition process to acquire a set of images that cover the key areas of the target scene. The images can be acquired by the terminal's image acquisition module, capturing a series of frames. The acquisition duration and frame rate can be dynamically configured based on terminal performance and network conditions. Image acquisition can be performed manually by the user or automatically in the background by the system upon receiving a request.

[0062] At box 306, terminal 102 sends a set of images to the server. To improve the accuracy of subsequent positioning, the terminal can preprocess the images, such as image quality filtering and keyframe selection. In some embodiments, after keyframe selection of the acquired image frames, once the selected keyframes reach the acquisition quantity threshold or the time window limit is reached, keyframe uploading to the server can be initiated. During the keyframe selection process, some low-quality image frames can be discarded, such as those that are significantly shifted compared to the previous keyframe, or those that are blurry, too dark, or overexposed.

[0063] At box 308, terminal 102 receives the target pose of the target anchor point in terminal 102 from server 104 based on the transmission of a set of images. The target pose is determined based on a set of images, a point cloud for the target scene, and the relative pose of the target anchor point with respect to the point cloud.

[0064] In some embodiments, after the terminal sends a set of acquired images to the server, the server performs registration and alignment processing based on the image set and a pre-constructed 3D point cloud of the target scene. The server uses computer vision techniques, such as feature point matching algorithms, to determine the corresponding camera pose of the image set in the point cloud space, thereby recovering the spatial position and orientation of the terminal in the 3D scene. Subsequently, the server combines the known relative pose information of the target anchor point with respect to the point cloud and maps the anchor point from the point cloud space to the terminal's local coordinate system through coordinate transformation, thereby generating the target pose of the anchor point in the terminal. This target pose generally includes 3D coordinates and orientation information, used to achieve accurate virtual-real fusion, anchor point display, or extended reality content rendering on the terminal side. Finally, the server returns the target pose information to the terminal, which then accurately recovers the position of the anchor point in the current scene in its local coordinate system, realizing cross-device and cross-platform sharing and spatially consistent presentation of the anchor point.

[0065] The set of images described above can be referred to as the first set of images, and the target anchor point can be referred to as the first anchor point. In some embodiments, the terminal 102 can further determine a second anchor point in the target scene. Next, the terminal 102 can acquire a second set of images for the target scene. Then, the terminal 102 can determine the reference relative pose of the second anchor point relative to each image in the second set of images. Next, the terminal 102 sends the second set of images and anchor point information for the second anchor point to the server, the anchor point information including the reference relative pose of the second anchor point relative to each image in the second set of images. The server can determine the relative pose of the second anchor point relative to the point cloud based on the received information. Additionally, if the second anchor point is an anchor point for another target scene, and the second set of images is an image for another target scene, then a point cloud for the other target scene can be constructed on the server.

[0066] In some embodiments, in order to enable these devices to operate in a unified coordinate space, the coordinate systems of both the terminal and the server can be transformed to the anchor point coordinate system, thereby achieving coordinate system unification.

[0067] This method integrates image acquisition and uploading driven by anchor point sharing requests with server-side processing based on point cloud and anchor point relative pose, making the spatial reconstruction of target anchor points in heterogeneous terminals more accurate. This effectively improves the expressiveness of cross-device spatial consistency and the collaborative experience in multi-user scenarios, thereby enhancing the user experience.

[0068] The above combination Figure 3 A schematic diagram of another example method for processing anchor points according to some embodiments of the present disclosure is described below; in conjunction with Figure 4 The illustration shows a flowchart of an example process for cross-vendor device mapping according to some embodiments of the present disclosure. Figure 4 Example process 400 can be generated by Figure 1 The terminal 102 and server 104 shown herein, or any suitable device, shall be executed.

[0069] Example flow 400 describes an example flow for cross-vendor device mapping. When the user triggers the keyframe acquisition process or the system automatically detects the need for keyframe acquisition, keyframe acquisition begins at point 402. The acquired keyframes represent image frames of the current scene and are used for subsequent 3D reconstruction. At point 404, the terminal judges the acquired keyframes to determine if the number of acquired keyframes has reached the keyframe acquisition threshold or time window limit, i.e., the maximum allowed acquisition time. If neither condition is met, other operations are performed at point 418. If either condition is met, keyframe data is uploaded to Vendor Y's cloud or server at point 406. At point 408, the upload is complete, and Vendor Y's cloud or server receives all uploaded data. After the upload is complete, at point 410, cloud-based keyframe mapping begins on Vendor Y's cloud or server. This process extracts features from the image, estimates camera pose, and generates dense or sparse point cloud models. After keyframe mapping is completed at point 412, a 3D point cloud map related to the target scene is generated. This map will be used for the spatial positioning of anchor points and subsequent multi-device spatial synchronization. At point 414, a mapping relationship for anchor points is established on the cloud or server based on the generated 3D map and the received anchor point information (such as anchor point identifiers). This includes the anchor point, vendor type, 3D point cloud map, and the relative pose of the anchor point relative to the 3D point cloud map. After establishing the anchor point mapping relationship, the entire cross-vendor device cloud mapping process is completed at point 416.

[0070] The above combination Figure 4 A flowchart describing an example process for cross-vendor device mapping according to some embodiments of this disclosure is provided below. Figure 5 The illustration shows a flowchart of an example process for cross-vendor device positioning according to some embodiments of the present disclosure.

[0071] Example flow 500 describes an example flow for cross-vendor device mapping. At 502, after receiving an anchor point sharing request, the terminal device reads the target anchor points previously shared by other devices or users from the cloud or server. Data for these anchor points typically includes anchor point identifiers, scene identifiers (IDs), the anchor point's relative pose to the point cloud, and associated vendor identifiers. At 504, it determines whether the current anchor point provider is vendor X, i.e., a target vendor different from the terminal device's vendor. If the current anchor point provider is not vendor X, other operations are performed at 514, such as directly retrieving anchor point information from the server. If the current anchor point provider is the target vendor, keyframe images of the current terminal device over a period of time are collected at 506 to assist cloud-based identification and anchor point matching. These keyframes should cover the main structure of the scene and have good visual feature richness. At 508, the terminal uploads the collected keyframe data to the server of the "vendor Y" where the target anchor point is located. Even if the current device does not belong to vendor Y, data upload can be completed through standard protocols, demonstrating the system's cross-platform compatibility. After receiving the image in the cloud, vendor Y extracts visual feature points from the uploaded image at position 510 and compares these features with existing anchor point mapping data to achieve keyframe matching, thus determining the anchor point's pose on the current device. Finally, at position 512, after completing anchor point matching and localization, the cloud returns the anchor point's spatial pose to the current terminal device. This pose represents the target anchor point's position in the current device's coordinate system, allowing the terminal to perform spatial anchor point presentation and interactive operations.

[0072] The above combination Figure 5 The illustration shows a flowchart of an example process for cross-vendor device positioning according to some embodiments of the present disclosure. The following is a description of this process in conjunction with... Figure 6 The illustration shows an example process of an X vendor device sharing an anchor point to a Y vendor device according to some embodiments of the present disclosure.

[0073] In Example 600, when vendor X's terminal device 608 needs to share an anchor point to vendor Y's terminal device 626, vendor X's end-side device 610 first creates the anchor point to be shared at 612, and then caches the image corresponding to the anchor point at 614. Then, vendor X's end-side device 610 uploads the anchor point and its corresponding map at 616, uploading the anchor point to vendor X's cloud service 602. At this point, vendor X's cloud service contains the uploaded anchor point 604 and the corresponding map 606. Then, at 618, it continues to upload the image to vendor Y's cloud service 638 and starts the mapping operation. Vendor Y's cloud service 638 receives the information uploaded from vendor X's end-side device 610, including the anchor point 640, keyframe 642, pose relative to the keyframe 644, and vendor source type 646, and performs the mapping operation based on this information. Y vendor's cloud service 638 constructs a 3D point cloud 650 based on the received keyframes using keyframe mapping 648. Then, based on the generated 3D point cloud and the received anchor point information, it establishes the relative pose of the anchor points in the point cloud and the mapping relationship related to the anchor points. Simultaneously, Y vendor's terminal device 628 on the Y vendor's end-side device 626 also receives a request from X vendor's end-side device 610 to share anchor points. Device 628 then performs keyframe acquisition at 630 and uploads data to Y vendor's cloud service 638 for anchor point localization at 632, such as a new set of keyframes. The uploaded data includes image upload 634 and anchor point loading 636. During anchor point localization, keyframe localization is performed at 652 to match the new set of keyframes uploaded by the terminal with the point cloud, and the pose of the anchor points on Y vendor's end-side device 628 is recovered by combining the relevant keyframe poses.

[0074] Meanwhile, when vendor X's edge device 610 needs to share anchor points with vendor X's edge device 620, device 620 only needs to read the anchor point and map information from vendor X's cloud service, load the anchor point locally on vendor X's edge device 620 at point 622, and restore the anchor point at point 624 to share the anchor point.

[0075] The above is combined Figure 6 The illustration shows an example process of an X vendor device sharing an anchor point to a Y vendor device according to some embodiments of the present disclosure. The following is a detailed description of this process. Figure 7 A schematic diagram illustrating an example process of a Y vendor device sharing an anchor point to an X vendor device according to some embodiments of the present disclosure.

[0076] In Example 700, when Y vendor's terminal device 702 needs to share an anchor point with X vendor's terminal device 720, Y vendor's end-side device 704 first creates the anchor point to be shared at 706, and then caches the image corresponding to the anchor point at 708. Then, Y vendor's end-side device 704 uploads the cloud anchor point at 710 and the image corresponding to the anchor point at 712, thus uploading the anchor point and image to Y vendor's cloud service 732. Upon receiving the information uploaded by Y vendor's end-side device 704, including the anchor point 734, keyframe 736, pose relative to the keyframe 738, and vendor source type 740, Y vendor's cloud service 732 performs mapping operations based on this information. Y vendor's cloud service performs keyframe mapping 742 based on the received keyframe to construct a 3D point cloud model 744, and then establishes the pose of the anchor point relative to the point cloud and the mapping relationship about the anchor point based on the generated 3D point cloud and the received anchor point information. Simultaneously, X vendor's terminal device 722 on the X vendor's side also receives the request to share anchor points from device 704. Device 722 then performs keyframe acquisition at 724 and uploads data to Y vendor's cloud service 732 at 726 for anchor point positioning. The uploaded data includes image upload 728 and anchor point loading 730. During positioning, keyframe positioning is performed at 746, which is responsible for matching the new set of keyframes uploaded by the terminal with the point cloud and restoring the anchor point's pose on X vendor's terminal device 722 by combining the relevant keyframe poses. Furthermore, when device 714 also needs to share anchor points, it can directly load anchor points from Y vendor's cloud service 732 at 716 and perform local anchor point restoration at 718.

[0077] Figure 8 A schematic block diagram of an apparatus for processing anchor points according to some embodiments of the present disclosure is shown. Figure 8 As shown, the device 800 includes an image receiving module 802, configured to receive a set of images of a target scene from a terminal at a server; a point cloud and anchor point relative pose determination module 804, configured to determine the relative pose of the point cloud of the target scene and the target anchor point in the target scene relative to the point cloud; and a target pose determination module 806, configured to determine the target pose of the target anchor point on the terminal based on a set of images, point cloud and relative pose.

[0078] In some embodiments, the target pose determination module 806 includes: a transformation relationship determination module configured to determine the transformation relationship between an image and a point cloud in a set of images based on a set of images and a point cloud; and a target pose determination module configured to determine the target pose of a target anchor point on the terminal based on the transformation relationship and the relative pose.

[0079] In some embodiments, the transformation relationship determination module includes: a feature point extraction module configured to extract multiple feature points of an image; and a transformation relationship calculation module configured to determine the transformation relationship between the image and the point cloud by matching the multiple feature points with the point cloud.

[0080] In some embodiments, where the terminal is a first terminal and the set of images is a first set of images, the apparatus 800 further includes: an image and anchor point information receiving module configured to receive from the second terminal a second set of images for a target scene and anchor point information for a target anchor point, the anchor point information including a reference relative pose of the target anchor point relative to each image in the second set of images; a point cloud determination module configured to generate a point cloud for the target scene based on the second set of images; and a relative pose calculation module configured to determine the relative pose of the target anchor point relative to the point cloud based on the point cloud and the reference relative pose.

[0081] In some embodiments, the point cloud determination module includes: a camera pose determination module configured to determine the camera pose for each image in the second set of images by matching feature points in the second set of images; and a point cloud generation module configured to generate a point cloud based on the camera pose and the matched feature points in the second set of images.

[0082] In some embodiments, the first image sensor in the first terminal for acquiring a first set of images and the second image sensor in the second terminal for acquiring two sets of images are different types of image sensors.

[0083] In some embodiments, the anchor point information further includes a target anchor point identifier for the target anchor point, and the device 800 further includes a mapping relationship determination module configured to determine the mapping relationship between the target anchor point identifier, the point cloud, and the relative pose of the target anchor point relative to the point cloud.

[0084] In some embodiments, the apparatus 800 further includes: a target anchor point identifier receiving module configured to receive a target anchor point identifier from a first terminal; a target anchor point identifier existence detection module configured to determine whether the target anchor point identifier exists in the mapping relationship; and an image acquisition request module configured to send a request to the first terminal for acquiring a first set of images in response to the existence of the target anchor point identifier in the mapping relationship.

[0085] In some embodiments, a set of images is a series of images within a predetermined time period.

[0086] Figure 9 A schematic block diagram of another apparatus for processing anchor points according to some embodiments of the present disclosure is shown. Figure 9As shown, the device 900 includes a request receiving module 902 configured to receive a request at a terminal for sharing a target anchor point in a target scene; an image acquisition module 904 configured to acquire a set of images of the target scene based on the request; an image sending module 906 configured to send the set of images to a server; and a target pose receiving module 908 configured to receive the target pose of the target anchor point in the terminal from the server based on the transmission of the set of images, wherein the target pose is determined based on the set of images, a point cloud of the target scene, and the relative pose of the target anchor point relative to the point cloud.

[0087] In some embodiments, where the request includes a target anchor point identifier, the image acquisition module 904 includes: a target anchor point identifier sending module, configured to send the target anchor point identifier to a server to determine whether the target anchor point exists in a mapping relationship, the mapping relationship being a mapping relationship between the target anchor point identifier, the point cloud, and the relative pose of the target anchor point relative to the point cloud; and an image input module, configured to acquire a set of images in response to the target anchor point being in a mapping relationship.

[0088] In some embodiments, where a set of images is a first set of images and a target anchor point is a first anchor point, the image acquisition module 904 further includes: a target anchor point confirmation module configured to determine a second anchor point in the target scene; a second set of image acquisition module configured to acquire a second set of images for the target scene; a reference relative pose determination module configured to determine the reference relative pose of the second anchor point relative to each image in the second set of images; and an image and anchor point information sending module configured to send the second set of images and anchor point information for the second anchor point to the server, wherein the anchor point information includes the reference relative pose of the second anchor point relative to each image in the second set of images.

[0089] Figure 10 A schematic block diagram of an example device 1000 that can be used to implement embodiments of the present disclosure is shown. Figure 1 The terminal 102 and server 104 can be implemented using device 1000. As shown, device 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) 1002 or loaded from storage unit 1008 into random access memory (RAM) 1003. RAM 1003 can also store various programs and data required for the operation of device 1000. CPU 1001, ROM 1002, and RAM 1003 are interconnected via bus 1004. Input / output (I / O) interface 1005 is also connected to bus 1004.

[0090] Multiple components in device 1000 are connected to I / O interface 1005, including: input unit 1006, such as keyboard, mouse, etc.; output unit 1007, such as various types of monitors, speakers, etc.; storage unit 1008, such as disk, optical disk, etc.; and communication unit 1009, such as network card, modem, wireless transceiver, etc. Communication unit 1009 allows device 1000 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0091] The various processes and procedures described above, such as methods 200, 300, and process 400, can be executed by processing unit 1001. For example, in some embodiments, methods 200, 300, and process 400 can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed on device 1000 via ROM 1002 and / or communication unit 1009. When the computer program is loaded into RAM 1003 and executed by CPU 1001, one or more actions of the example methods 200, 300, and process 400 described above can be performed.

[0092] This disclosure can be a method, apparatus, system, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of this disclosure.

[0093] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0094] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0095] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0096] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0097] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0098] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0099] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0100] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for processing anchor points, comprising: The server receives a set of images for the target scene from the terminal. Determine the point cloud of the target scene and the relative pose of the target anchor point in the target scene with respect to the point cloud; as well as Based on the set of images, the point cloud, and the relative pose, the target pose of the target anchor point on the terminal is determined.

2. The method according to claim 1, wherein determining the target pose of the target anchor point on the terminal comprises: Based on the set of images and the point cloud, determine the transformation relationship between the images in the set of images and the point cloud; as well as Based on the transformation relationship and the relative pose, the target pose of the target anchor point on the terminal is determined.

3. The method according to claim 2, wherein determining the conversion relationship between the images in the set of images and the point cloud comprises: Extract multiple feature points from the image; as well as The conversion relationship between the image and the point cloud is determined by matching the plurality of feature points with the point cloud.

4. The method according to claim 1, wherein the terminal is a first terminal, the set of images is a first set of images, and the method further comprises: The second terminal receives a second set of images for the target scene and anchor point information for the target anchor point, wherein the anchor point information includes the reference relative pose of the target anchor point relative to each image in the second set of images; Based on the second set of images, a point cloud for the target scene is generated; as well as Based on the point cloud and the reference relative pose, the relative pose of the target anchor point relative to the point cloud is determined.

5. The method of claim 4, wherein generating the point cloud for the target scene based on the second set of images comprises: The camera pose for each image in the second set of images is determined by matching feature points in the second set of images. as well as The point cloud is generated based on the camera pose and the feature points matched in the second set of images.

6. The method according to claim 4, wherein the first image sensor in the first terminal for acquiring the first set of images and the second image sensor in the second terminal for acquiring the second set of images are image sensors of different types.

7. The method of claim 4, wherein the anchor point information further includes a target anchor point identifier for the target anchor point, and the method further includes: Determine the mapping relationship between the target anchor point identifier, the point cloud, and the relative pose of the target anchor point relative to the point cloud.

8. The method according to claim 7, further comprising: Receive the target anchor point identifier from the first terminal; Determine whether the target anchor point identifier exists in the mapping relationship; as well as In response to the existence of the target anchor point identifier in the mapping relationship, a request for obtaining the first set of images is sent to the first terminal.

9. The method according to claim 1, wherein the set of images is a series of images within a predetermined time period.

10. A method for processing anchor points, comprising: Receive a request at the terminal to share the target anchor point in the target scene; Based on the request, obtain a set of images for the target scene; Send the set of images to the server; as well as Based on the transmission of the set of images, the target pose of the target anchor point in the terminal is received from the server. The target pose is determined based on the set of images, the point cloud of the target scene, and the relative pose of the target anchor point with respect to the point cloud.

11. The method of claim 10, wherein the request includes a target anchor point identifier of the target anchor point, and acquiring a set of images for the target scene includes: The target anchor point identifier is sent to the server to determine whether the target anchor point exists in a mapping relationship. The mapping relationship is the mapping relationship between the target anchor point identifier, the point cloud, and the relative pose of the target anchor point relative to the point cloud. as well as In response to the target anchor point in the mapping relationship, the set of images is acquired.

12. The method of claim 10, wherein the set of images is a first set of images, the target anchor point is a first anchor point, and the method further comprises: Determine the second anchor point in the target scene; Obtain a second set of images for the target scene; Determine the reference relative pose of the second anchor point relative to each image in the second set of images; as well as The server sends the second set of images and anchor point information for the second anchor point, the anchor point information including the reference relative pose of the second anchor point relative to each image in the second set of images.

13. An apparatus for processing anchor points, comprising: The image receiving module is configured to receive a set of images for a target scene from the terminal at the server. The point cloud and anchor point relative pose determination module is configured to determine the relative pose of the point cloud and the target anchor point in the target scene relative to the point cloud for the target scene. as well as The target pose determination module is configured to determine the target pose of the target anchor point on the terminal based on the set of images, the point cloud, and the relative pose.

14. An apparatus for processing anchor points, comprising: The request receiving module is configured to receive requests at the terminal for sharing target anchor points in the target scene; The image acquisition module is configured to acquire a set of images for the target scene; The image sending module is configured to send the set of images to the server; as well as The target pose receiving module is configured to receive the target pose of the target anchor point in the terminal from the server based on the transmission of the set of images. The target pose is determined based on the set of images, the point cloud of the target scene, and the relative pose of the target anchor point with respect to the point cloud.

15. An electronic device comprising: At least one processor; as well as A storage device for storing at least one program, which, when executed by the at least one processor, causes the at least one processor to implement the method according to any one of claims 1-12.

16. A computer-readable storage medium having a computer program stored thereon, the computer program implementing the method according to any one of claims 1-12 when executed by a processor.

17. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-12.

Images