Monocular Depth Estimation Using Size-Invariant Features
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Solution Overview
Problem
Existing depth estimation techniques, such as LiDAR or stereo cameras, are expensive and computationally intensive, making them impractical for consumer electronics and embedded devices.
Innovation Solution
The described techniques estimate the distance of an object from a camera using a single RGB camera, leveraging size-invariant and size-variant features to calculate distances without requiring specialized sensors, thus being computationally lightweight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR or stereo cameras are used for depth estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a single RGB camera to capture images, which are then processed to create depth information. Instead of using multiple specialized sensors (stereo cameras or LiDAR), the system creates a computational copy of depth data from standard 2D images through monocular depth estimation algorithms, thereby avoiding the need for complex multi-sensor hardware while achieving depth measurement functionality
Solution Approach 2:
The patent replaces mechanical/optical depth sensing systems (stereo cameras, LiDAR) with a computational approach using a single camera combined with image processing algorithms. The depth information is derived through computational methods rather than direct optical or mechanical measurement, substituting complex hardware with simpler hardware plus software processing
2Measurement precision
If LiDAR or stereo cameras are used for depth estimation, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system creates depth information computationally from standard RGB images rather than using energy-intensive active sensors like LiDAR. By copying depth data from 2D image information through algorithmic processing, the system avoids the high energy consumption associated with active illumination and complex sensor operation while maintaining depth estimation capability
3Measurement precision
If deep learning methods are used for depth map creation, then measurement precision is improved, but productivity decreases due to computational expense
Solution Approach 1:
The patent employs lightweight depth estimation algorithms that process only the essential features needed for depth calculation rather than performing exhaustive deep learning analysis. By applying partial processing to the image data—focusing on key depth-relevant features rather than complete scene understanding—the system achieves acceptable depth accuracy with significantly reduced computational overhead and faster processing speeds
Solution Approach 2:
The system adjusts algorithmic parameters to balance accuracy and speed, using simplified depth estimation models with optimized computational parameters. By changing the complexity parameters of the processing algorithm from high (full deep learning) to moderate (lightweight monocular depth estimation), the system achieves a practical trade-off between measurement precision and processing productivity suitable for embedded devices
Data Source
AI summary
In one embodiment, an apparatus includes accessing an image, captured by a first camera, of an object that includes a size-invariant feature and a size-variant feature and determining a size of the size-invariant feature in the image. The method further includes determining, based on the determined size of the size-invariant feature in the image, a distance between the camera and the object and determining a size of the size-variant feature in the image. The method further includes determining, based on the determined distance between the camera and the object and based on the determined size of the size-variant feature in the image, an actual size of the size-variant feature.