Sparse Light Field Representation via Depth Estimation
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Solution Overview
Problem
Light fields with high spatial-angular resolution require significant storage space and are difficult to process efficiently, as they often involve large datasets that need to be kept in memory.
Innovation Solution
A sparse representation method that estimates depth from high spatio-angular resolution light fields by storing depth estimates and error between reconstructions and images, allowing for compact storage and efficient reconstruction of the light field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high spatial-angular resolution light fields are captured using multiple high resolution images, then the light field quality and detail are improved, but the storage space requirements increase enormously
Solution Approach 1:
The patent extracts and stores only the essential depth information from the light field data, separating the critical structural information (depth estimates) from the redundant full-resolution image data. This extraction approach maintains the ability to reconstruct light fields while dramatically reducing storage requirements from hundreds of high-resolution images to compact depth maps and error representations.
Solution Approach 2:
The patent transforms the light field representation from storing complete high-resolution images to storing depth parameter estimates and error terms. This parameter transformation changes the data structure from pixel-intensive image arrays to compact depth maps, enabling efficient storage while preserving the essential geometric information needed for light field reconstruction.
2Loss of information
If full depth-of-field images for different viewpoints are merged and stacked in a 4D volume, then complete light field representation is achieved, but processing becomes difficult and memory-intensive
Solution Approach 1:
The patent creates a simplified copy of the light field information by storing depth estimates rather than complete image stacks. This copying approach captures the essential geometric structure of the light field in a compact form, enabling reconstruction without requiring the full 4D volume of high-resolution images to be loaded into memory for processing.
Solution Approach 2:
The patent adds a depth dimension to the representation by storing depth maps alongside error terms, transforming the problem from managing large 4D image volumes to working with compact depth-parameter representations. This dimensional transformation enables efficient processing by organizing information according to depth layers rather than spatial-viewpoint dimensions.
Data Source
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AI summary
The disclosure provides an approach for generating a sparse representation of a light field. In one configuration, a sparse representation application receives a light field constructed from multiple images, and samples and stores a set of line segments originating at various locations in epipolar-plane images (EPI), until the EPIs are entirely represented and redundancy is eliminated to the extent possible. In addition, the sparse representation application determines and stores difference EPIs that account for variations in the light field. Taken together, the line segments and the difference EPIs compactly store all relevant information that is necessary to reconstruct the full 3D light field and extract an arbitrary input image with a corresponding depth map, or a full 3D point cloud, among other things. This concept also generalizes to higher dimensions. In a 4D light field, for example, the principles of eliminating redundancy and storing a difference volume remain valid.