Guided Filtering for ToF Depth Boundary Accuracy
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Solution Overview
Problem
ToF camera systems often fail to accurately estimate depth at boundaries, such as edges of objects and reflectivity boundaries, limiting their use in applications like VR, AR, and vehicle navigation.
Innovation Solution
A depth estimation system using a guided filter enhances ToF depth estimation by incorporating brightness images, which are used to improve boundary detection and smooth flat planes, leveraging a guided filter to refine depth values based on brightness image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If ToF depth estimation is used, then depth measurement speed and range are improved, but measurement precision at boundaries deteriorates
Solution Approach 1:
The patent combines ToF depth image data with brightness image data through a guided filter to create an enhanced depth image. The guided filter merges the fast depth measurement capability of ToF with the precise boundary information from brightness images, resolving the contradiction between measurement speed and boundary accuracy.
Solution Approach 2:
The guided filter acts as an intermediary processing step that takes the rough but fast ToF depth image and the high-quality boundary information from brightness images, then produces an enhanced depth image that preserves both speed and precision advantages.
2Length of stationary object
If ToF depth estimation is used, then depth measurement range is improved, but measurement precision at reflectivity boundaries deteriorates
Solution Approach 1:
The patent merges ToF depth information with brightness image information using a guided filter. This combination allows the system to maintain the extended measurement range of ToF while correcting precision errors at reflectivity boundaries by leveraging the complementary strength of brightness data.
Solution Approach 2:
The guided filter modifies the depth values by incorporating brightness information, effectively changing the parameters used for depth calculation at boundary regions. This allows the system to maintain long-range measurement capability while improving local precision at reflectivity boundaries.
3Measurement precision
If guided filter is applied to enhance depth estimation, then measurement precision at boundaries is improved, but device complexity increases
Solution Approach 1:
The guided filter serves as an intermediary processing module that adds boundary enhancement capability without requiring complete system redesign. It processes the existing ToF and brightness images through a well-defined algorithmic framework, improving precision while keeping the added complexity manageable and modular.
4Measurement precision
If guided filter is applied to enhance depth estimation, then boundary detection accuracy is improved, but processing time increases
Solution Approach 1:
The guided filter applies partial processing by focusing computational effort primarily on boundary regions where precision is most needed, rather than uniformly processing all pixels. This selective approach improves boundary detection accuracy while minimizing the overall processing time penalty.
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
The enhanced depth estimation system provides more accurate boundary detection and smoother flat plane representation, improving the usability of ToF camera systems in various applications.
Implementation Method 1
One technique to measure depth is to directly or indirectly calculate the time it takes for a signal to travel from a signal source on a sensor to a reflective surface and back to the sensor. The time travelled is proportional to the distance from the sensor to the reflective surface. This travel time is commonly referred as time of flight (ToF).
Implementation Method 2
determining a weight for a target pixel of a depth image capturing an object based on the depth image and a brightness image capturing the object; determining whether the target pixel represents at least a portion of a boundary of the object based on the weight
Data Source
AI summary
A depth estimation system can use a guided filter to enhance depth estimation using brightness image. Light is projected onto an object. The object reflects at least a portion of the projected light. The reflected light is at least partially captured by an image sensor. The depth estimation system may generate a depth image based on a phase shift between the captured light and the projected light and generate a brightness image based on brightness of the captured light. The depth estimation system may use the guided filter to identify a pixel that represents at least a portion of a boundary of the object. The guided filter can determine a depth value of the pixel based on the value of the corresponding pixel in the brightness image. The depth estimation system can assign the depth value to the pixel and generates an enhanced depth image.