Affine-Invariant Spatial Mask for Active Depth Sensing
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Solution Overview
Problem
Existing active depth sensing techniques face challenges in accurately identifying spatial codes due to distortions caused by varying depths and surfaces, leading to inaccurate reconstruction of 3D representations.
Innovation Solution
A method involving a composite code mask with a carrier layer and a code layer, where the carrier layer provides reference markers robust to distortion, and the code layer is pre-shaped using a synthetic point spread function to enhance power efficiency and correct for distortions, allowing for accurate decoding of depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a spatial mask is projected onto a scene with varying depths, then depth information can be captured, but the pattern becomes distorted making accurate identification difficult
Solution Approach 1:
The spatial mask is segmented into a composite code mask with distinct layers: a carrier layer containing distortion-robust reference objects and a code layer containing depth-encoding codewords. This segmentation allows the reference objects to maintain identification accuracy while the code layer captures depth information, resolving the contradiction between measurement precision and reliability.
Solution Approach 2:
The carrier layer with reference objects acts as an intermediary that provides a stable reference framework. This intermediary layer is robust to distortion and enables accurate identification, which in turn allows correct interpretation of the code layer patterns even when distorted by varying depths.
2Use of energy by moving object
If the code mask is pre-shaped using a synthetic point spread function, then power efficiency increases, but the system complexity increases
Solution Approach 1:
The code mask is pre-shaped using a synthetic point spread function before projection. This preliminary action optimizes the distribution of optical energy, ensuring that the projected pattern delivers maximum power efficiency to the scene while maintaining the ability to accurately capture depth information, thus improving energy utilization without requiring complex real-time adjustments.
3Reliability
If reference objects are made robust to distortion, then identification accuracy improves, but the code density decreases
Solution Approach 1:
The mask is segmented into distinct functional layers: the carrier layer with distortion-robust reference objects and the code layer with high-density codewords. This segmentation allows each layer to optimize its specific function without compromising the other, maintaining both identification accuracy and code density.
Solution Approach 2:
The solution transitions to a two-dimensional layered structure where reference objects and codewords occupy different spatial layers. This dimensional separation allows the reference objects to be spaced adequately for robust identification while the code layer maintains high density, effectively resolving the trade-off between identification accuracy and code density.
Data Source
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AI summary
A method operational on a receiver device for decoding a codeword is provided. At least a portion of a composite code mask is obtained, via a receiver sensor, and projected on the surface of a target object. The composite code mask may be defined by a code layer and a carrier layer. A code layer of uniquely identifiable spatially-coded codewords may be defined by a plurality of symbols. A carrier layer may be independently ascertainable and distinct from the code layer and may include a plurality of reference objects that are robust to distortion upon projection. At least one of the code layer and carrier layer may have been pre-shaped by a synthetic point spread function prior to projection. The code layer may be adjusted, at a processing circuit, for distortion based on the reference objects within the portion of the composite code mask.