Stochastic Ranging for Fast Dense Depth Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computational stereo techniques face challenges in determining robust and efficient correspondences between pixels in images, particularly in dense reconstruction and calibrated monocular aerial modeling, which are hindered by runtime inefficiencies and sensitivity to low-quality imagery and changing scene conditions.
Innovation Solution
A stochastic method for fast stereoscopic ranging is introduced, involving the selection of image pairs, seeding of estimated range metrics, stochastic computation of local influences, aggregation of influences, and refinement of depth estimates, which iteratively processes and refines depth metrics using Z-buffering and cooperative search techniques to improve accuracy and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational stereo techniques are used to determine correspondences between pixels, then depth estimates can be generated, but runtime inefficiencies and sensitivity to low-quality imagery occur
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing depth estimates from previously processed image frames. These pre-computed depth estimates are then reused as initial values for current frame processing, eliminating the need to compute everything from scratch and significantly reducing runtime while maintaining accuracy
Solution Approach 2:
The patent applies local quality by processing only the necessary portions of the image data with full computational effort, while reusing pre-computed results for other areas. This selective processing approach optimizes the balance between accuracy and computational runtime by applying intensive computation only where needed
2Measurement precision
If dense reconstruction systems attempt to generate depth estimates for most or all pixels, then comprehensive scene structure is achieved, but computational complexity increases
Solution Approach 1:
The patent reduces computational complexity by pre-computing depth estimates for all pixels in advance and storing them. During actual processing, these pre-computed values are reused, allowing dense reconstruction without the full computational burden being applied at processing time
Solution Approach 2:
The patent uses copying by replicating and reusing pre-computed depth estimate data across multiple processing operations. Instead of recomputing depth estimates repeatedly, the system copies and applies previously computed results to multiple frames and processing stages
3Measurement precision
If iterative refinement processes are applied to improve depth metric accuracy, then estimation precision increases, but runtime and memory consumption increase
Solution Approach 1:
The patent performs preliminary computation of depth estimates and stores them before actual processing. This allows iterative refinement to start from pre-computed values rather than raw data, reducing the number of iterations needed and thereby reducing runtime while maintaining precision
Solution Approach 2:
The patent applies iterative refinement selectively only where needed based on local image quality and complexity. By identifying regions that require refinement and applying computation only there, the system maintains depth metric accuracy while reducing overall processing runtime and memory consumption
Data Source
AI summary
A stochastic method and system for fast stereoscopic ranging includes selecting a pair of images for stereo processing, in which the pair of images are a frame pair and one of the image is a reference frame, seeding estimated values for a range metric at each pixel of the reference frame, initializing one or more search stage constraints, stochastically computing local influence for each valid pixel in the reference frame, aggregating local influences for each valid pixel in the reference frame, refining the estimated values for the range metric at each valid pixel in the reference frame based on the aggregated local influence, and post-processing range metric data. A valid pixel is a pixel in the reference frame that has a corresponding pixel in the other frame of the frame pair. The method repeats n iterations of the stochastically computing through the post-processing.