Monocular Depth Estimation Model Uncertainty Quantification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monocular depth estimation models for autonomous driving struggle to quantify uncertainty in their output, which is crucial for reliable drivable range recognition. Current methods, such as Bayesian and ensemble methods, are inefficient for real-time vehicle applications and reduce the number of depth information cases.
Innovation Solution
A training apparatus and method that updates the weights of a monocular depth estimation model using a loss function group. This group includes a first loss function based on the depth distribution map and the depth estimation map, and optionally a second loss function derived from pose change information. The apparatus also estimates uncertainty by calculating the relative standard deviation of each pixel's depth estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Bayesian and ensemble methods are used to obtain uncertainty, then uncertainty quantification is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent uses a simple standard deviation calculation instead of complex Bayesian or ensemble methods. This disposable, computationally cheap approach provides sufficient uncertainty quantification for real-time autonomous driving without the heavy computational burden of more sophisticated methods.
Solution Approach 2:
The patent extracts only the essential uncertainty information (standard deviation) from the depth estimation output, discarding the need for complex probabilistic frameworks. This extraction of the core uncertainty metric enables efficient real-time processing while maintaining practical uncertainty awareness.
2Measurement precision
If classification task with entropy is used to obtain uncertainty, then uncertainty quantification is improved, but depth information cases are reduced
Solution Approach 1:
The patent replaces the classification-based entropy approach with a regression-based standard deviation calculation. This substitution maintains continuous depth information output while providing uncertainty quantification, avoiding the discretization inherent in classification tasks.
3Device complexity
If monocular depth estimation model is used, then device complexity is reduced, but uncertainty quantification capability deteriorates
Solution Approach 1:
The patent enables the monocular depth estimation model to self-quantify its own uncertainty through standard deviation calculation on its output. This self-service approach allows the simple model to provide both depth estimation and uncertainty information without requiring complex auxiliary structures or multiple models.
Data Source
AI summary
The present disclosure relates to an apparatus for training and causing autonomous driving control of a vehicle. The apparatus may comprise at least one processor, and a memory storing instructions, when executed by the at least one processor, cause the apparatus to obtain, based on a depth map obtained from a cluster of points at a target time point, a depth distribution map, obtain, based on an input image that is associated with the target time point and that is applied to a monocular depth estimation (MDE) model, a depth estimation map, update, based on a loss function group applied to the MDE model, a plurality of weights included in the MDE model, wherein the loss function group may comprise a first loss function that is obtained based on the depth distribution map and the depth estimation map, and output a signal indicating the updated plurality of weights.