Uncertainty-Aware Depth Fusion for Consistent Scene Estimation
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Solution Overview
Problem
Existing methods for depth estimation in computer vision and robotics, such as SLAM and CNN-based approaches, face limitations in accuracy and consistency, particularly in outdoor environments and with limited range and high power consumption of depth cameras.
Innovation Solution
An image processing system that probabilistically fuses depth estimates from a geometric reconstruction engine and a neural network architecture, using measurements of uncertainty to improve the accuracy and consistency of depth estimation, enabling efficient and reliable depth mapping in various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth camera is used for depth estimation, then depth measurement capability is improved, but power consumption increases and range is limited
Solution Approach 1:
The patent combines multiple depth estimation approaches (geometric reconstruction from multiple images and neural network-based monocular depth estimation) into a unified system that produces a single fused depth map, thereby achieving depth camera capabilities without relying solely on power-intensive dedicated depth sensors
Solution Approach 2:
The system creates a virtual depth representation by processing standard color images through geometric and neural network methods, effectively copying depth information from 2D images without requiring physical depth camera hardware
2Measurement precision
If geometric reconstruction with small-baseline stereo matching is used, then depth estimation is obtained, but global consistency is not preserved
Solution Approach 1:
The system uses iterative optimization where the fused depth map is refined through multiple passes, with each iteration incorporating feedback from both geometric constraints and neural network predictions to progressively improve global consistency while maintaining local accuracy
Solution Approach 2:
The patent creates a composite depth estimation approach by fusing two different depth estimation methods (geometric reconstruction and neural network prediction) with complementary strengths, where the geometric method provides local precision and the neural network provides global context
3Productivity
If CNN-based depth prediction is used, then depth map is generated, but blurring artifacts occur at depth borders
Solution Approach 1:
The patent introduces an intermediary fusion process that combines CNN-predicted depth maps with geometrically-reconstructed depth maps, where the geometric component acts as a mediator to sharpen depth borders and remove blurring artifacts while preserving the overall depth structure
Data Source
AI summary
An image processing system to estimate depth for a scene. The image processing system includes a fusion engine to receive a first depth estimate from a geometric reconstruction engine and a second depth estimate from a neural network architecture. The fusion engine is configured to probabilistically fuse the first depth estimate and the second depth estimate to output a fused depth estimate for the scene. The fusion engine is configured to receive a measurement of uncertainty for the first depth estimate from the geometric reconstruction engine and a measurement of uncertainty for the second depth estimate from the neural network architecture, and use the measurements of uncertainty to probabilistically fuse the first depth estimate and the second depth estimate.