Thermal Image Depth Estimation Using Shared Latent Space
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Solution Overview
Problem
Thermal cameras lack depth cues for effective depth perception, especially in dark scenes and long ranges, and existing depth cameras interfere with thermal signals, making them ineffective in bad weather and long-range applications.
Innovation Solution
A system utilizing a shared latent space trained by visible light and thermal images, along with text encoders and decoders, to generate depth information from thermal images, enhancing depth perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth cameras are used to obtain depth information, then depth perception is improved, but thermal signals are interfered with and effectiveness in dark scenes and long ranges deteriorates
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that processes thermal images to extract depth information indirectly, rather than using depth cameras that directly interfere with thermal signals. The model learns depth estimation from thermal image patterns without requiring active illumination that would contaminate the thermal data.
Solution Approach 2:
The patent replaces the mechanical/optical depth camera system with a computational approach using machine learning models that process thermal images. This substitution eliminates the need for additional illuminators while achieving depth estimation through algorithmic analysis of thermal patterns.
2Illumination intensity
If thermal cameras are used for night vision, then operation in dark scenes is improved, but depth cues are lacking and depth perception deteriorates
Solution Approach 1:
The patent uses a machine learning model as an intermediary to extract depth information from thermal images by learning the relationship between thermal patterns and depth. The model acts as a bridge that recovers depth cues that are not directly visible in thermal images but can be inferred through trained patterns.
Solution Approach 2:
The patent transforms thermal images into a different representation space where depth information becomes accessible. By applying learned transformations and processing in latent spaces, the system extracts depth parameters that are not directly present in the original thermal image domain.
3Measurement precision
If depth cameras are used for long-range depth measurement, then depth measurement capability is improved, but effectiveness in bad weather and long ranges deteriorates
Solution Approach 1:
The patent replaces active depth camera systems with a passive thermal imaging approach combined with machine learning. This substitution allows operation in bad weather conditions because thermal cameras passively detect heat signatures without being affected by rain, fog, or other environmental factors that interfere with active illumination-based depth cameras.
Data Source
AI summary
An apparatus can include an image encoder configured to be trained by a plurality of visible images captured by a visible light camera. The image encoder can be configured to output image encoder output. The apparatus can further include a text encoder configured to be trained by a plurality of text phrases. Each text phrase from the plurality of text phrases can be associated with an object with each visible image from the plurality of visible images. The text encoder can be configured to output text encoder output. The apparatus can further include a thermal encoder configured to be trained by a plurality of thermal images captured by a thermal camera. The thermal encoder can be configured to output thermal encoder output, the image encoder output, the text encoder output and the thermal encoder output collectively defining a shared latent space.