3D Scene Reconstruction with Motion-Aware Depth Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Reconstructing a three-dimensional scene with moving content, particularly distinguishing between global and local motion, poses challenges in accurately determining depth and relative locations for augmented or virtual reality applications.
Innovation Solution
A method involving stereoscopic image processing, camera tracking, and deep neural networks to dynamically adjust scene reconstruction based on local motion detection, using static or dynamic reconstruction techniques, and iterative pixel labeling to refine depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic scene reconstruction is used to handle moving content, then measurement precision of depth and location is improved, but device complexity and computational requirements increase
Solution Approach 1:
The system dynamically switches between static and dynamic reconstruction modes based on motion detection. When local motion is detected, dynamic reconstruction is activated; otherwise, static reconstruction is used. This dynamic adaptation resolves the contradiction by applying complex processing only when necessary, maintaining simplicity for static scenes while achieving precision for dynamic scenes.
Solution Approach 2:
The system changes the reconstruction parameters and algorithms based on scene motion characteristics. By detecting motion type (global vs. local) and adjusting reconstruction parameters accordingly, the system optimizes between computational complexity and measurement precision for different scene conditions.
2Manufacturing precision
If iterative pixel labeling is performed to refine depth maps, then manufacturing precision of 3D scene representation is improved, but loss of time increases
Solution Approach 1:
The system performs iterative pixel labeling selectively rather than universally. Pixels are labeled iteratively based on motion detection results and depth uncertainty, applying intensive processing only to regions that require refinement. This partial action approach maintains high precision where needed while reducing overall processing time.
3Adaptability or versatility
If local motion detection is implemented to distinguish global and local motion, then adaptability of scene reconstruction is improved, but device complexity increases
Solution Approach 1:
The motion detection system segments the scene into different regions and classifies motion types (global, local, or none) for each region. This segmentation allows the system to apply appropriate reconstruction techniques to different parts of the scene independently, improving adaptability while managing complexity through modular processing.
Data Source
AI summary
In one embodiment, a method includes identifying, in each image of a stereoscopic pair of images of a scene at a particular time, every pixel as either a static pixel corresponding to a portion of a scene that does not have local motion at that time or a dynamic pixel corresponding to a portion of a scene that has local motion at that time. For each static pixel, the method includes comparing each of a plurality of depth calculations for the pixel, and when the depth calculations differ by at least a threshold amount, then re-labeling that pixel as a dynamic pixel. For each dynamic pixel, the method includes comparing a geometric 3D calculation for the pixel with a temporal 3D calculation for that pixel, and when the geometric 3D calculation and the temporal 3D calculation are within a threshold amount, then re-labeling the pixel as a static pixel.