Attention-Based Depth Completion for Edge Devices
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Solution Overview
Problem
Conventional depth completion methods are computationally complex and require significant memory and power, making them unsuitable for resource-limited devices such as smartphones.
Innovation Solution
An artificial neural network (ANN) is used to receive an image and sparse depth measurements, extract multi-scale visual features, apply a self-attention mechanism to these features, and generate a dense depth map without iterative refinement processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional iterative processing approaches are used for depth completion, then measurement precision is improved, but device complexity and power consumption increase significantly
Solution Approach 1:
The patent segments the depth completion task into distinct processing stages: feature extraction from sparse depth measurements, attention-based feature refinement, and depth map generation. This segmentation allows each stage to be optimized independently, reducing overall computational complexity and power consumption while maintaining precision.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary component that processes extracted features before final depth map generation. This intermediary refines the features by attending to relevant spatial and channel information, improving measurement precision without requiring full iterative processing.
2Measurement precision
If conventional iterative processing approaches are used for depth completion, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex iterative mechanical processing with a streamlined neural network architecture that uses attention mechanisms. This substitution reduces device complexity by eliminating the need for multiple iterative passes while maintaining depth completion precision through learned feature representations.
Solution Approach 2:
The patent changes the processing parameters from iterative refinement loops to a single-pass attention-based transformation. By adjusting the computational approach from repeated iterations to a consolidated attention operation, the device complexity is reduced while preserving measurement precision.
3Measurement precision
If conventional iterative processing approaches are used for depth completion, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary feature extraction and attention-based refinement in a single forward pass through the neural network, eliminating the need for subsequent iterative processing steps. This preliminary action achieves both high precision and fast processing speed by completing all necessary computations in one evaluation.
Solution Approach 2:
The patent skips the traditional iterative refinement steps by using an attention mechanism that processes all necessary information in a single pass. This rushing through the processing pipeline maintains measurement precision while dramatically improving productivity and processing speed.
Data Source
AI summary
A processor-implemented method for attention-based depth completion includes receiving, by an artificial neural network (ANN), an input. The input includes an image and a sparse depth measurement. The ANN extracts multi-scale visual features of the input. The ANN applies a self-attention mechanism to the multi-scale visual features to generate a set of attended multi-scale visual features. The ANN generates a dense depth map based on the set of attended multi-scale visual features.