Frontend-Driven HLOD Sub-tree Generation for Infrastructure Models
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Solution Overview
Problem
Traditional infrastructure modeling techniques require significant processing and storage resources to generate and store exhaustive HLOD trees and tiles, leading to inefficient utilization and functional limitations, such as unnecessary resource consumption and imposed maximum levels of detail.
Innovation Solution
Implementing a frontend-driven, incremental HLOD sub-tree creation and dynamic tile generation approach, where the infrastructure modeling frontend module creates a local HLOD sub-tree based on initial metadata and refines it with tile contents, allowing tiles to be generated on demand and stored only when needed, without an artificial maximum level of detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single, exhaustive HLOD tree is created to define all spaces and levels-of-detail, then complete coverage of the infrastructure model is achieved, but significant processing and storage resources are consumed
Solution Approach 1:
The patent divides the exhaustive HLOD tree into multiple incremental HLOD sub-trees, each representing a portion of the infrastructure model. Each sub-tree is created only when needed for specific display purposes, rather than pre-generating the entire tree. This segmentation allows the system to maintain complete coverage capability while reducing overall resource consumption by creating only the necessary portions.
Solution Approach 2:
The system performs preliminary analysis of the infrastructure model to identify spaces and potential HLOD sub-trees before actual display requests. This allows the backend to prepare metadata and structure information in advance, so when a client requests a view, the appropriate HLOD sub-tree can be quickly generated without full model processing each time.
2Adaptability or versatility
If all possible tiles are generated and stored in the tile cache, then any client device can display any portion at any LOD, but storage resources are significantly consumed
Solution Approach 1:
Instead of uniformly generating all possible tiles for the entire model, the system generates tiles locally for specific HLOD sub-trees based on actual client display requests. Each HLOD sub-tree contains only the tiles necessary for that particular portion of the model at relevant levels-of-detail, rather than storing every possible tile combination across the whole model.
Solution Approach 2:
The backend performs preliminary generation of HLOD sub-trees and their associated tiles based on anticipated display needs and client requests. This allows the system to have tiles ready in advance for commonly viewed areas while avoiding generation of tiles for portions that won't be displayed, balancing versatility with storage efficiency.
3Loss of energy
If a maximum LOD is imposed to terminate the HLOD tree, then processing resources are reduced, but users cannot zoom in beyond the imposed maximum
Solution Approach 1:
The system dynamically determines the appropriate LOD and HLOD sub-tree depth based on each client device's display capabilities, network conditions, and specific viewing requirements. Rather than imposing a static maximum LOD, the system can adaptively generate HLOD sub-trees at any depth as needed, allowing users to zoom to arbitrary LODs when appropriate while avoiding unnecessary processing for displays that don't require high detail.
4Reliability
If the entire infrastructure model is analyzed to create the HLOD tree, then comprehensive model coverage is achieved, but processing time is increased
Solution Approach 1:
The system segments the infrastructure model analysis into discrete portions corresponding to different HLOD sub-trees. Instead of analyzing the entire model at once, the backend analyzes and generates HLOD sub-trees incrementally as clients request specific views. This maintains comprehensive coverage capability while distributing processing over time and reducing peak processing time.
Solution Approach 2:
The system performs preliminary lightweight analysis of the infrastructure model to establish the overall structure, spaces, and potential HLOD sub-tree divisions. This initial analysis is much faster than full model processing, and subsequent HLOD sub-tree generation builds on this preliminary structure, reducing the time required for each individual sub-tree creation.
Data Source
AI summary
In example embodiments, techniques are provided for frontend-driven, incremental HLOD sub-tree creation and dynamic generation of tiles. An infrastructure modeling frontend module (e.g., a iModel.js Frontend module) of a frontend application may incrementally create a local HLOD sub-tree, beginning with an initial HLOD sub-tree based on initial metadata provided without tile contents, and refined based on metadata provided with tile contents, from an infrastructure modeling backend module (e.g., a iModel.js Backend module) of a backend application. The local HLOD sub-tree of each infrastructure modeling frontend module may differ based on the individual tiles requested and the tile refinement strategies employed. The infrastructure modeling backend module may not maintain an HLOD tree, and simply generates tiles upon request, serving them and storing them in a tile cache incase they are needed again.