Monocular Depth Estimation Using Geometry Hints for Real-Time AR
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Solution Overview
Problem
Traditional depth estimation models in augmented reality sacrifice accuracy for computation cost, leading to high latency and breaking the perception of real-time interactive content.
Innovation Solution
A geometry-informed depth estimation model that uses low-cost geometry hints to improve depth estimation accuracy while maintaining computational speed, leveraging a feature encoder, matching network, and depth decoder to generate accurate per-frame depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional depth estimation models are used to improve accuracy, then depth estimation accuracy is improved, but computation cost increases and estimation speed decreases
Solution Approach 1:
The depth estimation process is segmented into two distinct stages: a fast low-cost geometry model that generates initial depth hints at high speed, and a refined machine learning model that processes these hints to produce accurate depth maps. This segmentation allows each component to optimize for its specific function, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The low-cost geometry model performs preliminary depth estimation to generate depth hints before the main depth estimation model processes the final output. This preliminary action provides a head start on the computation, allowing the more accurate but slower model to work from pre-processed information, thereby maintaining both speed and accuracy.
2Measurement precision
If traditional depth estimation models are used to improve accuracy, then depth estimation accuracy is improved, but latency increases and breaks the perception of real-time interactive content
Solution Approach 1:
By segmenting the depth estimation into a fast preliminary geometry model and a refined accuracy model, the system achieves low latency in the first stage while maintaining high accuracy in the second stage, preventing the perception break that occurs with high latency.
Solution Approach 2:
The geometry model performs preliminary depth estimation quickly to provide initial depth hints, reducing the time burden on the subsequent accurate model and thereby reducing overall latency to maintain real-time perception.
3Productivity
If low-cost geometry model is used to maintain computational speed, then computational speed is maintained, but depth estimation accuracy decreases
Solution Approach 1:
The system merges the outputs of two different models: the fast low-cost geometry model and the more accurate machine learning model. By combining their strengths through the depth hint mechanism, the system achieves both computational speed and depth estimation accuracy that neither model could achieve alone.
Solution Approach 2:
The depth hints from the geometry model serve as an intermediary that bridges the fast preliminary estimation and the final accurate depth map. This intermediary provides a computational shortcut that guides the more accurate model, allowing it to achieve high precision without sacrificing the speed benefits of the geometry model.
Data Source
AI summary
A depth estimation model leverages a geometry-rendered depth map from a low-cost geometry model to provide depth hints. The model is trained and configured to input a time series of frames including a target frame. The time series of images are captured as monocular video data by a camera assembly. Applying the model includes: applying a feature encoder to extract visual features forming a feature map for each frame, matching features across the features maps forming a cost volume, obtaining a geometry-rendered depth map from the low-cost geometry model of the scene based on a pose of the target frame, modifying the cost volume based on the geometry-rendered depth map, and applying a depth decoder to the modified cost volume to generate the depth map for the target frame. A client device implementing the model may generate virtual content using the depth map to display the target frame of the scene augmented with the virtual content.