Monocular Depth Estimation Using Patch Masking Consistency
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Solution Overview
Problem
Existing technologies face challenges in accurately estimating depth from monocular images without relying on costly stereo imaging and sufficient correct answer data, particularly in applications like autonomous vehicles and augmented reality.
Innovation Solution
A deep learning-based apparatus and method that utilizes a camera and processor to perform deep learning processes on monocular images, dividing them into patches, masking partial regions, and training a deep learning model to reduce deviations between initial and partial depth values, using techniques like dimension and gradation conversion, and adjusting parameters to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general 2D camera is used to monitor the outside of the vehicle, then the cost is low, but the depth value cannot be accurately determined
Solution Approach 1:
The image is divided into multiple patches, and the model processes each patch independently to estimate depth values. This segmentation allows the single 2D camera to achieve depth estimation capability by analyzing local regions separately and aggregating results, resolving the contradiction between using simple cameras and achieving accurate depth measurement.
Solution Approach 2:
The patent transforms 2D image data into 3D depth information by training the deep learning model to predict depth values from 2D patches. This dimensional transformation enables depth estimation without requiring stereo cameras or additional sensors, maintaining device simplicity while improving measurement precision.
2Measurement precision
If stereo image is used to more accurately estimate the depth value, then the depth estimation accuracy is improved, but the cost increases
Solution Approach 1:
The patent creates multiple views of the same image by dividing it into patches and processing them through the same deep learning model. This copying approach simulates the effect of multiple cameras (like stereo imaging) using a single camera, achieving accurate depth estimation without the cost of additional hardware.
Solution Approach 2:
The patent changes the processing parameters by applying different masking patterns to image patches during training. This allows the model to learn depth estimation from various perspectives and conditions, achieving stereo-like accuracy from monocular input without requiring actual stereo camera hardware.
3Measurement precision
If many correct answer data are used to accurately estimate the depth value, then the depth estimation accuracy is improved, but the data acquisition cost increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the model on synthetic data with known depth values, then fine-tuning it on real images. This preliminary preparation reduces the need for large amounts of expensive annotated real-world depth data, achieving accurate depth estimation with fewer costly data acquisitions.
Solution Approach 2:
The patent uses synthetic rendered images as an intermediary between training data and real-world applications. These synthetic images provide ground truth depth labels without requiring expensive real-world depth measurement equipment, serving as a cost-effective training resource that bridges the gap between simulation and reality.
Data Source
AI summary
An apparatus for estimating a depth is introduced. The apparatus may comprise a camera configured to capture an image may comprise an object, and a processor configured to perform, based on a deep learning model, a deep learning process associated with the image, obtain, based on the deep learning process associated with the image, a first depth value, obtain a partial image by masking a partial region of the image, perform, based on the deep learning model, a deep learning process associated with the partial image, obtain, based on the deep learning process associated with the partial image, a second depth value, train the deep learning model to reduce a deviation between the first depth value and the second depth value, and estimate, based on the trained deep learning model, a depth of the object.