Deterministic Sampling for Interactive Image Synthesis
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Solution Overview
Problem
Existing image synthesis methods fail to consistently continue the image generation process when changing the region of interest interactively, leading to compromised convergence and inefficiencies in both sequential and parallel computing environments.
Innovation Solution
A system and method that partition the problem domain, assign samples using a deterministic sequence, and allow for adaptive convergence speed without altering existing partitions, enabling interactive and arbitrary changes to the region of interest while maintaining convergence by using highly uniform point sequences and deterministic sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the region of interest is changed interactively during image synthesis, then the computing power can be focused on the region of interest, but the convergence of the image synthesis process is compromised
Solution Approach 1:
The image domain is partitioned into multiple subdomains or regions, allowing independent sampling strategies to be applied to different regions. This segmentation enables focused computing power on regions of interest while maintaining overall convergence through systematic partitioning of the sampling space.
Solution Approach 2:
Different sampling strategies and sample allocations are applied to different regions based on their importance. Regions of interest receive higher sampling density and computational resources, while less important regions use coarser sampling, achieving local optimization without compromising global convergence.
2Reliability
If the number of samples per partition is increased to improve convergence, then the convergence speed is improved, but the computing time and resources increase
Solution Approach 1:
The sampling parameters such as the number of samples per partition and the partitioning strategy are dynamically adjusted based on the region's importance and convergence requirements. This allows optimization of computing time by allocating more samples only where necessary for convergence.
Solution Approach 2:
Instead of uniformly increasing samples across the entire domain, the method applies excessive sampling (more samples than minimum required) only to critical regions of interest, while using partial sampling (minimum required samples) in less critical regions, thereby reducing overall computing time while maintaining convergence.
3Adaptability or versatility
If the partitioning is changed to focus on a new region of interest, then the computing power can be redirected, but the existing sampling progress is lost
Solution Approach 1:
The partitioning scheme is designed in advance to allow for future region changes without requiring complete re-sampling. Pre-computed sample sequences and partition structures are prepared that can be adaptively reused when regions of interest change, preserving prior sampling progress.
Solution Approach 2:
The sampling process maintains continuity by using deterministic sample sequences that can be continued from where they left off, even when partitioning is adjusted to focus on new regions. This allows the sampling action to continue usefully without restarting, preserving progress while adapting to new regions of interest.
Data Source
AI summary
A system, method, and computer program product are provided for focusing computing power to a region of interest that can be changed interactively and arbitrarily during the process of image synthesis. In operation, a problem domain is partitioned utilizing a first selected technique. Additionally, a number of samples to be drawn per partition are assigned utilizing a second selected technique. Furthermore, the assigned number of samples are drawn for each partition, where the samples are generated by only one deterministic sample sequence. Still yet, the partitioning, assigning, and drawing are capable of being repeated such that existing partitions and assignments are capable of remaining unchanged during sampling and a convergence speed is adapted without compromising convergence in at least one of a sequential computing environment or a parallel computing environment. In this way, the convergence of image synthesis is not compromised. In fact, the image synthesis process may converge to the same solution that would have been obtained without interaction.