Virtual Scene Object Loading Method for Resource Optimization
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Solution Overview
Problem
Existing technologies consume a large quantity of hardware resources to render objects in virtual scenes, leading to significant memory and CPU/GPU usage, causing performance issues in devices with limited capacity.
Innovation Solution
An object loading method that determines visible spaces within an acquisition range and loads only objects with a visible distance not exceeding a threshold, dynamically loading and unloading scene content to minimize resource usage, using a multi-level grid structure based on visible distances to optimize rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all objects in the virtual scene are loaded for rendering, then the completeness of the virtual scene is improved, but the memory and hardware resource consumption increases significantly
Solution Approach 1:
The virtual scene is divided into multiple visible spaces based on the acquisition range of the image acquisition device. Each visible space contains a subset of objects that can be seen from different positions. This segmentation allows the system to load only the objects relevant to the current viewing position, reducing memory and hardware resource consumption while maintaining scene completeness through dynamic switching between visible spaces.
Solution Approach 2:
The system dynamically determines which visible space to load based on the real-time position of the image acquisition device. As the device moves to different positions, the system dynamically switches between different visible spaces, loading only the objects that are currently visible. This dynamic approach ensures that the virtual scene remains complete and responsive while minimizing resource consumption by avoiding static pre-loading of all objects.
2Measurement precision
If visible spaces are divided into multiple types based on visible distance thresholds, then the precision of object selection is improved, but the device complexity increases
Solution Approach 1:
Different visible spaces are assigned different types based on their visible distance thresholds. Each type corresponds to a specific range of visible distances, allowing the system to select objects with appropriate precision for each local region. This local quality approach enables precise object selection within each visible space while keeping the overall system manageable by processing spaces in discrete, categorized units.
Data Source
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AI summary
The present application discloses an object loading method, a device, a storage medium, and an electronic device. The method comprises: determining, in a virtual scene, a visible space within an acquisition range of an image acquisition apparatus, the image acquisition apparatus being an acquisition apparatus located at a first position in the virtual scene; determining, on the basis of the first position and in the visible space, a target sub-space located within a visible distance threshold indicated by a target type among multiple types, each type in the multiple types indicating one visible distance threshold for an object in the sub-space of the virtual scene; acquiring an object in the target sub-space that has a visible distance not greater than the visible distance threshold indicated by the target type as an object to be rendered; and loading the object to be rendered into a memory resource of a user terminal having a target application installed, the user terminal being used to render an image of the virtual scene. The present application solves the technical problem in the related art in which rendering objects in a virtual scene occupies too many hardware resources.