Single Image Depth Estimation via Range Classification
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Solution Overview
Problem
Existing single image depth estimation (SIDE) methods using deep neural networks face challenges in generalization across different datasets due to domain-specific models and inefficiencies in large network architectures, which limits their performance and flexibility.
Innovation Solution
A two-stage robust SIDE framework that classifies input images into predefined depth ranges and applies optimized SIDE networks for each range, utilizing an efficient encoder-decoder architecture with depth-wise separable convolutional layers and upproj layers with skip connections to generate accurate depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single deep CNN model is trained for specific datasets, then the model achieves good performance on that dataset, but the model has poor generalization ability to other datasets
Solution Approach 1:
The patent applies universality by training a single deep CNN model that can handle multiple datasets (indoor and outdoor scenes) simultaneously. The model is designed with universal features that generalize across different domains, eliminating the need for separate domain-specific models while maintaining good performance on both indoor and outdoor depth estimation tasks.
2Measurement precision
If very deep CNN architecture is used for SIDE, then the model achieves good depth estimation performance, but the network size becomes very large and efficiency decreases
Solution Approach 1:
The patent applies parameter changes by optimizing the CNN architecture parameters to achieve a balance between depth estimation accuracy and processing efficiency. The model uses appropriate depth, filter sizes, and layer configurations that provide good performance without requiring excessively deep architectures, thus maintaining reasonable network size and computational efficiency.
3Measurement precision
If fully connected layers are used in SIDE network, then the network can achieve good performance, but a sliding-window strategy is required during testing which reduces flexibility
Solution Approach 1:
The patent applies the extraction principle by removing fully connected layers from the network architecture. Instead, the model uses only convolutional layers that maintain spatial information and allow for flexible processing of images of various sizes. This extraction of the problematic component eliminates the need for sliding-window strategies during testing, thereby improving flexibility while maintaining depth estimation performance.
Data Source
AI summary
A method and system for determining depth information of an image are herein provided. According to one embodiment, the method includes receiving an image input, classifying the input image into a depth range of a plurality of depth ranges, and determining a depth map of the image by applying depth estimation based on the depth range into which the input image is classified.