Light Field Super-Ray Segmentation Without Dense Depth Estimation
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Solution Overview
Problem
Existing light field editing methods are computationally expensive and require dense depth estimation, making them inefficient for real-time user interaction and editing on devices with limited computational power.
Innovation Solution
A method for processing light field images using super-rays, which groups perceptually similar rays across multiple views through a clustering technique that relies on color and spatial distances, reducing the need for dense depth estimation and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense depth estimation is used for light field editing, then segmentation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the light field data by grouping rays into super-rays based on spatial and angular proximity, creating coarse clusters that reduce the number of elements requiring depth estimation. This segmentation approach maintains adequate segmentation accuracy while significantly reducing computational complexity by processing clustered ray groups rather than individual rays.
Solution Approach 2:
The patent applies depth estimation selectively to representative rays within each super-ray cluster rather than performing dense depth estimation for all rays. This partial action approach achieves sufficient segmentation accuracy for editing applications without the full computational burden of dense depth estimation.
2Manufacturing precision
If dense depth estimation is performed, then editing precision is improved, but processing time increases
Solution Approach 1:
By segmenting rays into super-ray clusters based on spatial and angular proximity, the patent reduces the number of depth estimation operations required. This segmentation maintains adequate editing precision by ensuring that rays within each cluster share similar geometric properties, while dramatically reducing processing time through fewer computational operations.
Solution Approach 2:
The patent copies depth information from representative rays to other rays within the same super-ray cluster, avoiding redundant depth estimation computations. This copying approach preserves editing precision for clustered rays while significantly reducing processing time by eliminating repeated calculations.
3Productivity
If super-rays representation is used, then computational efficiency is improved, but data representation complexity increases
Solution Approach 1:
The patent merges multiple rays into super-ray clusters by combining their spatial and angular coordinates, creating a unified representation that reduces data volume. This merging improves computational efficiency by processing clustered data rather than individual rays, while the standardized super-ray data structure keeps representation complexity manageable.
Solution Approach 2:
The super-rays representation serves multiple functions simultaneously: it enables ray clustering for reduced computation, provides a framework for selective depth estimation, and supports light field editing operations. This multi-functionality improves computational efficiency across various operations while using a single standardized data structure that limits representation complexity.
Data Source
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AI summary
The present disclosure concerns a method for processing a light field image comprising a set of image views. The method is remarkable in that it comprises: selecting (101) a set of pixels, named centroids, within a given image view in said light field image, each centroid being associated with a unique label and a unique light ray; de-projecting said centroids, that delivers a set of points in a 3D space; re-projecting said points of said set of points in a 3D space in at least one other image view in said light field image, which is different from said reference view, that delivers a set of re-projected centroids; and determining (102), for each pixel, being associated with a light ray, within a neighborhood of a re-projected centroid, a label related to one of the centroids, said determining relying on a proximity criterion in term of color between pixels and centroids, and a proximity criterion in term of spatial distances between pixels and re-projected centroids; and updating (103), for a given centroid, a color value associated with said given centroid, and spatial coordinates of said given centroid within said given image view, wherein said updating comprises: de-projecting pixels in said at least one other image view that are associated with a same label of said given centroid, delivering a set of obtained points; and re-projecting said obtained points into said given image view, delivering a set of projected points; and averaging both color values associated with said projected points and spatial coordinates associated with said projected points for defining updated information related to said given centroid.