Single Image Depth Estimation via Visual Descriptor Mapping
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Solution Overview
Problem
Current image processing technologies face challenges in efficiently estimating depth information from a single image, which is essential for applications like e-commerce and shipping, where accurate object dimensions and visualizations are required.
Innovation Solution
A machine-learning based system that calculates visual and depth descriptors from a reference database, creating a cross-domain map to translate RGB images into depth maps, allowing for depth estimation in real-time from single images without pre-existing depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used, then processing speed is slow, but depth estimation accuracy is insufficient
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with a machine learning-based system that uses neural networks to estimate depth. The system trains on labeled datasets and learns complex patterns to directly predict depth maps from input images, achieving both high accuracy and fast processing speeds that traditional methods cannot achieve simultaneously
Solution Approach 2:
The patent transforms the depth estimation problem by changing the approach from pixel-by-pixel analysis to feature-based learning. The system extracts key visual features and uses learned transformations to map these features to depth information, fundamentally changing how depth is computed to achieve superior performance
2Measurement precision
If multiple images are used for depth estimation, then accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and processes only the most critical visual features from images rather than analyzing all pixels. By identifying and working with salient features such as edges, corners, and texture patterns, the system achieves accurate depth estimation from single images without the computational burden of processing entire image datasets
Solution Approach 2:
The system performs preliminary feature extraction and pre-computes transformation matrices during training on labeled datasets. This preliminary action stores learned relationships between visual features and depth information, allowing rapid inference on new images without time-consuming real-time analysis
3Manufacturing precision
If depth information is added to images, then 3D visualization quality improves, but data storage and processing complexity increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that bridges 2D images and 3D depth information. The model learns complex non-linear relationships between image pixels and depth values, transforming the problem from direct complex 3D reconstruction to a more manageable pattern recognition task that produces accurate depth maps with manageable computational complexity
Data Source
AI summary
During a training phase, a machine accesses reference images with corresponding depth information. The machine calculates visual descriptors and corresponding depth descriptors from this information. The machine then generates a mapping that correlates these visual descriptors with their corresponding depth descriptors. After the training phase, the machine may perform depth estimation based on a single query image devoid of depth information. The machine may calculate one or more visual descriptors from the single query image and obtain a corresponding depth descriptor for each visual descriptor from the generated mapping. Based on obtained depth descriptors, the machine creates depth information that corresponds to the submitted single query image.