Real-Time 3D Scene Reconstruction Using Keyframe Selection
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Solution Overview
Problem
Current video coding technologies struggle to efficiently produce 3D models and estimate camera positions for RGB-D sensors, particularly in real-time applications like virtual reality and augmented reality, where accurate and simultaneous localization and mapping are crucial.
Innovation Solution
Implementing a method that uses RGB-D cameras to select keyframes, adjust them based on probability variance values, and fuse color and depth information to provide a virtual representation of the scene, enabling real-time 3D reconstruction and camera pose estimation within a 3D coordinate system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional video coding technologies are used to produce 3D models from RGB-D sensors, then the processing complexity is reduced, but the accuracy of camera pose estimation and 3D reconstruction deteriorates
Solution Approach 1:
The patent segments the video stream into keyframes and non-keyframes, processing only keyframes for 3D reconstruction. This segmentation allows the system to maintain high accuracy by selectively processing important frames while reducing overall computational complexity through the use of temporal prediction for non-keyframes.
Solution Approach 2:
The patent performs preliminary action by pre-identifying and selecting keyframes based on motion magnitude and other criteria before the actual 3D reconstruction process. This preliminary selection optimizes the reconstruction accuracy by ensuring that only frames with sufficient information content are used as reference points.
2Manufacturing precision
If all frames are processed for 3D reconstruction, then the reconstruction quality is improved, but the processing time increases
Solution Approach 1:
The patent divides the video processing into keyframe processing and non-keyframe processing. Keyframes are fully processed for 3D reconstruction, while non-keyframes utilize temporal prediction and motion compensation, significantly reducing processing time while maintaining acceptable reconstruction quality.
Solution Approach 2:
The patent applies partial action by processing only a subset of frames (keyframes) in full detail for 3D reconstruction, while using simplified processing for non-keyframes. This selective approach achieves acceptable reconstruction quality with reduced processing time compared to processing all frames equally.
3Productivity
If keyframes are selected based on motion magnitude, then the processing efficiency is improved, but the selection complexity increases
Solution Approach 1:
The patent uses parameter changes by evaluating multiple criteria (motion magnitude, time intervals, scene changes) to determine keyframe selection. This multi-parameter approach improves processing efficiency by systematically identifying important frames while managing selection complexity through defined thresholds and weights for each parameter.
4Object-affected harmful factors
If depth values are combined using probability variance, then the noise reduction is improved, but the computational complexity increases
Solution Approach 1:
The patent replaces simple averaging mechanisms with probability variance-based fusion for combining depth values from multiple keyframes. This substitution improves noise reduction by accounting for the reliability of each depth measurement, while managing computational complexity through efficient variance calculation and thresholding.
Data Source
AI summary
Various implementations are disclosed of producing a 3-dimensional model of a scene. Various method, electronic device, or system implementations use RGB-D camera to provide RGB-D video content or periodic aligned RGB images and depth images to localize camera spatial position(s) defined in a three dimensional (3D) coordinate system or reconstruct a 3D virtual representation of a current camera frame in the 3D coordinate system, each in real time.