Frustum-Aware Digital Asset Suggestion System
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Solution Overview
Problem
Programmers face inefficiencies when adding assets to a scene, as they must manually select, scale, and orient numerous assets without consideration for the scene context, leading to wasted time on irrelevant assets.
Innovation Solution
A camera frustum aware suggestion system that determines scene data within a camera's view frustum, analyzes traits, and suggests digital objects based on correlations, providing context-specific suggestions for content creators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual asset selection methods are used, then programmers have full control over asset placement, but the time required to create scenes increases significantly due to manual selection and placement of each asset
Solution Approach 1:
The system enables self-service by automatically analyzing the scene context and generating asset suggestions without requiring manual intervention. The frustum-aware suggestion system autonomously identifies relevant assets based on the camera view and scene traits, allowing the system to serve itself rather than requiring programmer input for each asset selection decision.
Solution Approach 2:
The system performs preliminary action by pre-analyzing the scene context and preparing asset suggestions before the programmer needs to make placement decisions. By determining traits from the frustum content and pre-generating relevant asset recommendations, the system eliminates the need for time-consuming manual browsing and selection during the actual scene creation process.
2Adaptability or versatility
If comprehensive asset lists are provided without context consideration, then all possible assets are available for selection, but programmers waste time filtering out irrelevant assets that do not match the scene
Solution Approach 1:
The system applies local quality by tailoring asset suggestions to the specific local context of each scene. Rather than providing a uniform comprehensive list, the frustum-aware system analyzes the local scene traits and camera view to generate context-specific recommendations, ensuring that the asset suggestions are locally adapted to match the particular scene being created.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting asset suggestions based on detected scene parameters such as frustum content, scale, and traits. The suggestion system modifies the asset recommendations in response to changes in scene context, maintaining adaptability while reducing the time needed to filter irrelevant assets by only presenting contextually appropriate options.
3Manufacturing precision
If individual asset placement and scaling is done manually, then precise control over each asset is achieved, but the complexity of the scene creation process increases
Solution Approach 1:
The system introduces an intermediary by inserting a suggestion system between the programmer and the asset library. This intermediary analyzes the scene context and provides curated asset recommendations with appropriate placement, scaling, and orientation suggestions, thereby maintaining precision while reducing the complexity of manual asset management through intelligent mediation.
Data Source
AI summary
A method of determining a suggested digital object to place into a 3D environment is disclosed. Scene data within a frustum volume of a camera within a 3D environment is determined. The scene data includes a set of digital objects that are located within the frustum volume. A set of traits is determined based on the scene data. At least one suggested digital object is suggested for placing into the 3D environment based on a correlation between the suggested digital object and the set of traits.