Wavelet Depth Prediction for Low-Compute Image-Based Mapping
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Solution Overview
Problem
Existing methods for determining depth in augmented reality applications, such as using LIDAR sensors, are costly and synchronization challenges exist, necessitating a need for accurate depth prediction from images without relying on expensive hardware.
Innovation Solution
A depth prediction model utilizing wavelet decomposition to encode images into feature maps, iteratively refine depth maps with wavelet coefficients, and implement binary masking to minimize computational costs while maintaining high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors are used to determine depth, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent uses a camera to capture images and creates a computational copy of the depth information through image processing and neural network analysis, replacing the need for physical LIDAR sensors. The system processes visual data to generate depth maps, effectively copying the depth measurement function from expensive hardware to software-based image analysis.
Solution Approach 2:
The patent replaces the mechanical/optical LIDAR sensing system with a computational image processing system. Instead of using light detection and ranging hardware, the system uses software-based depth prediction models that analyze standard images to infer depth information, substituting physical sensing mechanisms with algorithmic processing.
2Measurement precision
If LIDAR sensors are used to determine depth, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent makes the camera serve multiple functions: it captures both standard images for visual display and depth information for spatial understanding. By processing the same image data through different computational pathways, the system eliminates the need for separate LIDAR hardware, reducing overall device complexity while maintaining depth measurement capability.
Solution Approach 2:
The patent merges the depth sensing function with the existing camera system. Instead of adding separate LIDAR sensors, the system combines depth prediction algorithms with standard image processing pipelines, allowing a single camera to provide both visual and depth information for augmented reality applications.
3Measurement precision
If traditional depth prediction methods are used, then computational accuracy is maintained, but computational cost increases
Solution Approach 1:
The patent segments the depth prediction process into multiple stages with varying computational intensities. The system first processes images at lower resolutions to obtain coarse depth information, then progressively refines depth accuracy at higher resolutions only where needed. This segmentation allows the system to maintain overall depth prediction accuracy while significantly reducing total computational energy requirements.
Solution Approach 2:
The patent applies partial computation strategies where full high-resolution depth processing is performed only on regions of the image that require precise depth information. For areas where coarse depth estimation suffices, the system uses reduced computational processing, thereby maintaining adequate depth prediction accuracy while minimizing overall computational energy consumption.
Data Source
AI summary
A depth prediction model for predicting a depth map from an input image is disclosed. The depth prediction model leverages wavelet decomposition to minimize computations. The depth prediction model comprises a plurality of encoding layers, a coarse prediction layer, a plurality of decoding layers, and a plurality of inverse discrete wavelet transforms (IDWTs). The encoding layers are configured to input the image and to downsample the image into feature maps including a coarse feature map. The coarse depth prediction layer is configured to input the coarse feature map and to output a coarse depth map. The decoding layers are configured to input the feature maps and to predict wavelet coefficients based on the feature maps. The IDWTs are configured to upsample the coarse depth map based on the predicted wavelet coefficients to the final depth map at the same resolution as the input image.