3D Environment Model Staleness Detection for Dynamic Objects
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Solution Overview
Problem
Current 3D models of real-world environments are limited in accurately representing dynamic objects, leading to stale representations that are not updated in real-time, resulting in inaccurate image reconstructions.
Innovation Solution
A system and method that classify objects as static or dynamic, applying visual effects to indicate staleness in 3D environment models by processing images from cameras to detect and update dynamic objects' representations, ensuring accurate and real-time visual indication of changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D models are generated using images captured by cameras, then the environment representation is created, but the representations of dynamic objects become stale when not in view, leading to accuracy degradation
Solution Approach 1:
The system dynamically updates the 3D environment model by continuously processing new images and detecting changes in dynamic objects. The model transitions from static to dynamic representation, where dynamic objects are selectively updated based on their detection in new images, ensuring accuracy without requiring complete re-scan of the entire environment.
Solution Approach 2:
The system pre-identifies dynamic objects in the environment and maintains their representations in the 3D model even when not currently visible. By having a preliminary understanding of which objects are dynamic, the system can proactively update their positions when they reappear, preventing staleness rather than reacting to it.
2Adaptability or versatility
If multiple representations of dynamic objects are maintained in 3D model, then object detection flexibility is improved, but model complexity and processing requirements increase
Solution Approach 1:
The system segments objects in the environment into static and dynamic categories. This segmentation allows differential handling: static objects maintain stable representations while dynamic objects have their representations selectively updated. The segmentation reduces overall model complexity by applying different update strategies to different object types.
Solution Approach 2:
The system applies different quality levels and update frequencies to different parts of the environment model. Dynamic objects that are frequently changing receive higher update priority and more detailed tracking, while static objects maintain simpler representations. This local differentiation optimizes the balance between adaptability and complexity.
Data Source
Figure 1A~1B
Figure 2A
Figure 2B
AI summary
Disclosed is system (100) comprising server(s) (102) configured to: receive plurality of images of real-world environment captured by camera(s) (104); process plurality of images to detect plurality of objects present in real-world environment and generate three-dimensional environment model of the real-world environment; classify each of plurality of objects as either static object or dynamic object; receive current image(s) of real-world environment; process current image(s) to detect object(s) represented therein; determine whether or not object(s) is/are from amongst plurality of objects; determine whether object(s) is static object or dynamic object when it is determined that object(s) is/are from amongst plurality of objects; and for each dynamic object that is represented in three-dimensional environment model but not in current image(s), apply first visual effect to representation of said dynamic object in three-dimensional environment model for indicating staleness of said representation.