2D to 3D Depth Map Generation Using SIFT Word-Depth Gradient Dictionary
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Solution Overview
Problem
Existing methods for transforming 2D images into 3D are slow due to the need for analyzing considerable reference images and require large memory capacity, as they rely on pre-storing images to infer depth information.
Innovation Solution
A device and method using a word-depth gradient dictionary generated through a training process, which stores SIFT descriptors and depth gradient information, allowing for efficient transformation of 2D images into 3D by obtaining and matching color patches from input images to generate depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference images are pre-stored and analyzed to infer depth information, then depth information can be obtained, but processing speed becomes very slow and memory capacity requirement increases
Solution Approach 1:
The patent applies preliminary action by pre-training a neural network model offline to learn the mapping relationship between 2D image features and depth information. During actual depth estimation, the pre-trained model directly processes input images without requiring reference image search, thus achieving both accurate depth information and high processing speed.
Solution Approach 2:
The patent replaces the mechanical reference image matching system with a neural network-based computational system. Instead of manually comparing input images against stored reference images, the neural network automatically learns and applies depth estimation patterns, significantly improving processing efficiency while maintaining accuracy.
2Measurement precision
If reference images are pre-stored and analyzed to infer depth information, then depth information can be obtained, but memory capacity requirement increases
Solution Approach 1:
The patent extracts only the essential depth estimation knowledge from reference images during an offline training phase, storing it compactly within the neural network model parameters. This eliminates the need to store large quantities of actual reference images in memory, reducing memory capacity requirements while preserving depth information accuracy.
Solution Approach 2:
The patent creates a compressed computational representation (neural network model) that captures the depth estimation patterns from reference images. This model copy contains the essential information needed for depth estimation without requiring the original large set of reference images to be stored in memory during operation.
3Measurement precision
If considerable amounts of reference images are analyzed individually, then depth information can be inferred, but processing speed becomes very slow
Solution Approach 1:
The patent performs the time-consuming analysis of reference images in advance during model training, allowing the trained neural network to make rapid depth estimations without repeating the reference image analysis process for each new input image.
Solution Approach 2:
The patent replaces the slow mechanical process of individual reference image analysis with a trained neural network that has already learned from reference images, enabling rapid parallel processing of depth estimation without the sequential bottlenecks of traditional matching methods.
Data Source
AI summary
A method and device for transforming 2D images into 3D are disclosed. The disclosed device includes a dictionary storage unit configured to store a word-depth gradient dictionary; a color patch obtainer unit configured to obtain color patches from an input image; a matching word search unit configured to transform each of the color patches obtained by the color patch obtainer unit into a SIFT descriptor form and search for words closest to the SIFT descriptors of the obtained color patches from among the words of the word-depth gradient dictionary; a matching depth gradient obtainer unit configured to obtain depth gradient information of the words matching the obtained color patches from the word-depth gradient dictionary; and a depth map generation unit configured to compute a depth from the obtained matching depth gradient for each of the obtained color patches and generate a depth map.