Foreground Obstacle Removal Using Point Cloud Depth Data
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Solution Overview
Problem
Existing image editing techniques require human interaction and are labor-intensive for removing undesirable foreground structures from images, as they need manual labeling and expertise, especially when dealing with large collections of images.
Innovation Solution
A system and method using point cloud and depth map data to automatically identify and remove foreground obstacles by generating a point cloud from multiple images captured from different positions, allowing for the separation of background subject matter from undesirable structures without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing image editing techniques are used to remove undesirable structures, then the structures can be removed from images, but human interaction and manual labeling are required which makes the process labor-intensive
Solution Approach 1:
The system performs self-service by automatically identifying and removing undesirable structures without requiring human intervention. The point cloud generation module autonomously processes multiple images to create a 3D representation, the identification module automatically detects undesirable structures, and the image generation module autonomously produces the final cleaned image, eliminating the need for manual labeling operations.
Solution Approach 2:
The system performs preliminary action by generating a point cloud from multiple images before the actual obstacle removal process. This pre-processing step creates a three-dimensional representation of the scene that enables automatic identification of undesirable structures, allowing the system to prepare all necessary data structures in advance and process multiple images efficiently in batch mode.
2Ease of operation
If manual labeling techniques are used, then undesirable structures can be identified and removed, but the process requires expertise and skills that increase complexity
Solution Approach 1:
The system replaces the mechanical system of manual human labeling with an automated computational system. Instead of relying on human operators to visually identify and label undesirable structures, the invention uses algorithmic processing of point cloud data to automatically detect and remove obstacles, substituting human cognitive operations with automated image processing and pattern recognition algorithms.
Solution Approach 2:
The point cloud serves as an intermediary that bridges the gap between raw image data and the final processed output. By converting multiple images into a three-dimensional point cloud representation, the system creates an intermediate data structure that makes it easier to automatically identify undesirable structures through spatial analysis, simplifying the overall processing pipeline.
3Productivity
If traditional inpainting techniques are used, then individual images can be processed, but processing large collections of images is inefficient
Solution Approach 1:
The system merges multiple images into a unified point cloud representation, allowing simultaneous processing of entire image collections rather than handling individual images separately. By combining the data from multiple images into a single three-dimensional structure, the system can identify and remove undesirable structures across all images in one unified operation, dramatically increasing processing throughput for large batches.
Solution Approach 2:
The system transitions from two-dimensional image processing to three-dimensional point cloud analysis, adding a spatial dimension to the processing workflow. This dimensional change enables more efficient batch processing by allowing the system to analyze spatial relationships across multiple images simultaneously, identifying patterns and structures that would be difficult to detect in individual 2D images.
Data Source
AI summary
A system having a non-transitory storage medium, wherein the non-transitory storage medium contains a first image captured at a first position relative to background subject matter, wherein undesirable structure is interposed between the first position and the background subject matter, and a second image captured at a second position, different from the first position, relative to the background subject matter wherein the undesirable structure is interposed between the second position and the background subject matter; a point cloud generation module coupled to the non-transitory storage medium, wherein the point cloud generation module generates a point cloud in response to the first image and the second image, and wherein the point cloud generation module stores the point cloud in the non-transitory storage medium; an identification module coupled to the non-transitory storage medium for retrieving the point cloud and identifying within the point cloud the undesirable structure and the background subject matter; and an image generation module coupled to the identification module for generating a third image in response to the background subject matter within the point cloud, wherein the image generation module stores the third image in the non-transitory storage medium.