Single Image Depth Estimation via Neural Network Synthesis
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Solution Overview
Problem
Current depth estimation methods either rely on expensive and hard-to-obtain high-quality depth labels or require binocular cameras, which are costly and prone to errors due to camera settings, limiting their accuracy.
Innovation Solution
A method that uses a single image to synthesize a corresponding image via a neural network, performing binocular stereo matching to estimate depth without needing binocular cameras, thereby reducing hardware overhead and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binocular cameras are used for depth estimation, then depth information can be obtained, but hardware costs increase and errors due to camera settings occur
Solution Approach 1:
The patent synthesizes a second image from a single input image using a neural network, creating a virtual binocular image pair without requiring physical binocular cameras. This copying approach eliminates hardware overhead while enabling stereo matching-based depth estimation
Solution Approach 2:
The patent replaces the mechanical binocular camera system with a computational approach using neural networks to generate synthetic images. This substitution eliminates hardware complexity while maintaining depth estimation functionality through learned image synthesis and stereo matching
2Measurement precision
If high-quality depth labels are used for training, then depth estimation accuracy improves, but data acquisition costs and difficulty increase
Solution Approach 1:
The patent employs semi-supervised learning where the model uses a small amount of labeled data to train the image synthesis network, then leverages this synthesized data to train the depth estimation network. This self-service approach eliminates the need for large quantities of expensive high-quality depth labels
Solution Approach 2:
The patent changes the training paradigm from requiring extensive labeled depth data to using semi-supervised learning with synthesized images. By changing the data utilization parameters and training strategy, the system achieves high accuracy without relying on costly high-quality depth labels
Data Source
AI summary
A depth estimation method includes: taking a single image as a first image in binocular images, and obtaining a second image in the binocular images based on the first image via a first neural network; and obtaining depth information corresponding to the first image via a second neural network by performing binocular stereo matching on the first image and the second image.