Depth Estimation via Attention Maps and Uncertainty Regression
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Solution Overview
Problem
Existing depth sensors face challenges in accurately determining depth information, particularly with transparent objects and restricted distance ranges, leading to incomplete depth maps and failure when presented with different sensor configurations, due to their static nature and inability to adapt to diverse spatial contexts.
Innovation Solution
A method involving the generation of RGB and depth images with varying pixel densities, merging them to create an attention map, determining uncertainty scores, and applying depth values using a unified ordinal vector and regression method, while incorporating Neutrosophic sets for uncertainty handling and sensor-agnostic depth completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth sensors are used to capture depth information, then three-dimensional spatial data can be obtained, but the sensors cannot detect transparent objects and have restricted distance range resulting in incomplete depth maps
Solution Approach 1:
The patent segments the depth completion task into multiple processing stages: generating initial depth maps from sparse sensor data, creating attention maps to identify uncertain regions, and applying regression models to fill missing depth values. This multi-stage segmentation allows the system to address specific limitations at each stage rather than attempting to solve all problems simultaneously.
Solution Approach 2:
The patent introduces attention maps as an intermediary component that mediates between the sparse depth sensor data and the final complete depth map. The attention map identifies regions with high uncertainty or missing depth values and guides the regression model to focus computational resources on these critical areas, thereby improving overall depth map completeness.
2Productivity
If convolutional neural networks are used for depth completion, then sparse depth data can be processed, but the static nature of the network limits adaptability to diverse spatial contexts and different sensor configurations
Solution Approach 1:
The patent transforms the static CNN approach into a dynamic system by introducing attention mechanisms that adaptively weight different regions of the input data based on their importance. The attention maps are dynamically generated for each input scene, allowing the network to adapt its processing focus to diverse spatial contexts and different sensor configurations rather than applying fixed convolutional operations uniformly across all inputs.
Solution Approach 2:
The patent employs parameter changes by using regression models that can adjust their parameters based on the specific characteristics of the input depth map and attention map. This allows the system to adapt to different sensor configurations and spatial contexts by modifying model parameters rather than requiring a completely different network architecture for each scenario.
3Reliability
If multiple depth sensors with different configurations are used, then more complete depth coverage can be achieved, but models trained for specific sensor configurations fail when presented with input from different sensors
Solution Approach 1:
The patent creates a universal depth completion framework that can process inputs from multiple sensor configurations through the use of attention maps and regression models. The attention mechanism universally identifies uncertain regions regardless of sensor type, and the regression model universally fills missing values based on learned patterns from training data that encompasses multiple sensor configurations, making the system multi-functional across different sensor types.
Solution Approach 2:
The patent uses parameter changes to adapt the model to different sensor configurations. By training the regression model on diverse sensor data and allowing parameters to adjust based on input characteristics, the system can generalize across different sensor types without requiring separate models for each configuration.
4Measurement precision
If high pixel density RGB images are generated, then detailed color information is obtained, but processing complexity and computational power requirements increase
Solution Approach 1:
The patent applies local quality by using attention maps to identify specific regions that require detailed processing. Instead of uniformly processing all high-resolution RGB image data, the system focuses computational resources on local regions identified as having high uncertainty or importance, thereby reducing overall computational power consumption while maintaining precision where it matters most.
Solution Approach 2:
The patent employs partial action by selectively processing only the portions of the high-resolution image data that are necessary for depth completion. The attention mechanism identifies and processes only the critical regions rather than applying full processing to the entire high-resolution image, reducing computational overhead while maintaining adequate detail where needed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate and robust depth estimation across various sensors and environments, achieving high accuracy levels and supporting seamless 3D scene understanding in both indoor and outdoor settings with reduced power consumption.
Implementation Method 1
determining a phase shift between the illuminated scene and the reflected light at the different phases
Data Source
AI summary
A method for determining depth information of objects in a scene, includes: generating RGB images having a first pixel density using an RGB camera; generating a depth image having a second pixel density using a depth camera, the second pixel density being lower than the first pixel density; generating an attention map using the RGB images with a high pixel density; merging the RGB images and the depth image to generate a merged image; determining an uncertainty score of each pixel of a plurality of pixels in the merged image; determining a threshold for the uncertainty score based on a level of uncertainty in the plurality of pixels; determining a depth value for each pixel of the plurality of pixels exceeding the threshold of the uncertainty score; applying the depth value of each pixel of the plurality of pixels in the merged image; and generating the scene with the depth information based on the depth value.