Neural Network Depth Estimation Using Odometry Constraints
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Solution Overview
Problem
Conventional structure-from-motion algorithms are unreliable for estimating depth without a model of the environment and are limited in estimating depth with a single camera, as they can only provide depth up to a scaling factor.
Innovation Solution
A deep learning-based approach using neural networks, specifically deep recurrent neural networks (RNNs) and convolutional long short-term memory (LSTM) networks, to estimate scene factors like pixel depth, velocity, class, and optical flow simultaneously, without relying on environmental models, by combining multiple loss functions and utilizing odometry information to constrain camera motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional structure-from-motion algorithms are used to estimate depth, then the estimation can be obtained, but the reliability is poor and depth can only be estimated up to a scaling factor without environmental models
Solution Approach 1:
The patent changes the fundamental parameters of depth estimation by transitioning from scaling-factor-based estimates to metric-value-based estimates. This is achieved by integrating odometry information (translation length) as an additional constraint parameter, allowing the neural network to estimate depth in actual metric units rather than relative scaling factors, thereby improving both precision and reliability
Solution Approach 2:
The patent introduces odometry information as an intermediary element that mediates between the image data and depth estimation. The odometry-based translation length serves as a reference that constrains the neural network's depth predictions, enabling reliable metric depth estimation without requiring environmental models
2Measurement precision
If conventional structure-from-motion algorithms are used, then depth estimation is possible, but the device complexity increases due to requirements for environmental models
Solution Approach 1:
The patent extracts and removes the requirement for environmental models from the depth estimation system. By using odometry information instead, the system achieves metric depth estimation without needing to build or rely on 3D environmental models, thereby reducing system complexity while maintaining precision
Solution Approach 2:
The patent substitutes the conventional structure-from-motion mechanical/computational system with a neural network-based system constrained by odometry. This replacement simplifies the system by eliminating the need for complex environmental modeling while achieving reliable metric depth estimation
Data Source
AI summary
A mechanism is described for facilitating depth and motion estimation in machine learning environments, according to one embodiment. A method of embodiments, as described herein, includes receiving a frame associated with a scene captured by one or more cameras of a computing device; processing the frame using a deep recurrent neural network architecture, wherein processing includes simultaneously predicating values associated with multiple loss functions corresponding to the frame; and estimating depth and motion based the predicted values.