Neural Depth Map Confidence Estimation From Photometric Error
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Solution Overview
Problem
Existing depth estimation techniques for images lack the ability to output a confidence level or uncertainty measure associated with estimated depth maps, which is crucial for reliable use in applications like autonomous vehicles.
Innovation Solution
A neural network is trained using self-supervised learning to determine ground truth depth values and photometric errors based on paired images from cameras with known geometric relationships, allowing it to output both depth values and confidence levels for each pixel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained to estimate depth values for an image, then depth estimation capability is achieved, but the network cannot output confidence levels associated with each pixel
Solution Approach 1:
The neural network output is segmented into two separate channels: one for depth value estimation and another for confidence level prediction. This segmentation allows the network to independently optimize both depth accuracy and uncertainty quantification without one interfering with the other, thereby resolving the contradiction between achieving precise depth estimation and providing confidence information.
Solution Approach 2:
The neural network is designed with multi-functionality to simultaneously perform depth estimation and confidence level prediction. By incorporating multiple output heads or layers that serve different purposes (depth map generation and uncertainty estimation), the system achieves both functions within a single unified model, eliminating the need for separate systems and enabling both precision and information retention.
2Productivity
If depth estimation is performed using traditional techniques, then processing speed is maintained, but the system cannot provide uncertainty measures for downstream tasks
Solution Approach 1:
The neural network is pre-trained during the learning phase to simultaneously predict both depth values and their associated confidence levels. This preliminary action during training establishes the dual-output capability before deployment, ensuring that when the system operates at full processing speed, it already has the reliability information embedded without requiring additional post-processing steps that would slow down operation.
Data Source
AI summary
A method may include receiving an image of a scene, inputting the image into a trained neural network, determining an estimated depth map for the image based on a first output of the neural network, the estimated depth map comprising a depth value for each pixel of the image, and determining a confidence level of the depth value for each pixel of the image based on a second output of the neural network.