Dynamic Structured Light Depth Sensing for Low-Power AR
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Solution Overview
Problem
Augmented Reality (AR) systems face challenges in accurately determining the positions of physical objects in real-world environments to integrate virtual objects effectively, as existing depth sensing methods are inefficient in power consumption and accuracy, especially in head-mounted displays with limited resources.
Innovation Solution
A depth sensing system using structured light patterns, which includes projectors emitting known light patterns and detectors capturing reflections, employs a technique to reduce power consumption by alternating between dense and sparse patterns, projecting partial patterns in consecutive frames, and using triangulation to compute depth maps, allowing for efficient and accurate depth determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dense structured light pattern is projected continuously, then depth sensing accuracy is improved, but power consumption increases
Solution Approach 1:
The system alternates between projecting dense structured light patterns and sparse patterns in a periodic manner. During dense pattern projection, high-quality depth data is captured. During sparse pattern projection, power consumption is reduced. This periodic switching allows the system to maintain acceptable depth sensing accuracy while significantly reducing overall power consumption compared to continuous dense pattern projection.
Solution Approach 2:
The system dynamically adjusts the pattern density based on operational requirements and power availability. The projector can switch between dense and sparse patterns adaptively, optimizing the balance between measurement quality and energy consumption in real-time conditions.
2Measurement precision
If a dense structured light pattern is projected, then depth map quality is improved, but processing time increases
Solution Approach 1:
The system uses periodic dense pattern projection interspersed with sparse pattern projection. This approach captures sufficient depth information during dense pattern intervals while reducing the total time spent projecting and processing dense patterns, thereby improving depth map quality without excessive processing time overhead.
Solution Approach 2:
The system performs preliminary depth mapping using sparse patterns to establish a baseline depth model. This preliminary action allows subsequent dense pattern projections to focus on updating specific regions rather than processing the entire scene, reducing overall processing time while maintaining depth map quality.
3Use of energy by moving object
If sparse patterns are used for power saving, then power consumption is reduced, but depth sensing accuracy deteriorates
Solution Approach 1:
The system periodically projects dense patterns to refresh and correct the depth map, ensuring accuracy is maintained. Between these periodic dense projections, sparse patterns are used to extend battery life. This periodic reinforcement strategy ensures that depth sensing accuracy does not deteriorate significantly even when sparse patterns dominate the operation.
Solution Approach 2:
The system uses feedback from detected changes in the environment to determine when to switch from sparse to dense patterns. When motion or changes are detected, the system triggers a dense pattern projection to update the depth map, ensuring accuracy is maintained when it matters most while conserving power during static periods.
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 enhances the accuracy of depth sensing while significantly reducing power consumption, enabling effective integration of virtual objects with physical environments in AR systems, particularly in resource-constrained devices like head-mounted displays.
Implementation Method 1
The projectors emit may structured light of known patterns into an environment and the detector may detect reflections of the emitted light from objects in the environment
Implementation Method 2
a depth map that represents the three-dimensional features of objects in the environment may be generated by triangulating the emitted light and detected reflected light
Data Source
AI summary
In one embodiment, a system includes at least one projector configured to project a plurality of projected patterns onto a scene, the projected patterns including a first projected pattern that includes a plurality of first projected features, a camera configured to capture a plurality of images including a first detected pattern corresponding to a reflection of the first projected pattern, and one or more processors configured to: compute a depth map of the scene based on the first projected pattern, the first detected pattern, and relative positions of the camera and the at least one projector, project, using the projector, a second projected pattern comprising a plurality of second projected features onto a portion of the scene, where the second projected pattern is more sparse than the first projected pattern, and capture, using the camera, a second detected pattern corresponding to a reflection of the second projected pattern.