ERP Depth Estimation for Camera-Agnostic Monocular Images
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Solution Overview
Problem
Depth estimation for images captured by a monocular camera is challenging due to the dependency on the camera lens/type used for training data, leading to decreased accuracy when using different lenses, and training with multiple types of cameras and lenses is costly and time-consuming.
Innovation Solution
A camera-agnostic approach is employed by converting captured images to equirectangular (ERP) format, training a vision model using regular images with ground-truth depth labels, and projecting these images onto a spherical surface for ERP space estimation, eliminating the need for costly ERP images with ground-truth depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth estimation is performed using a monocular camera with specific lens/type, then depth estimation accuracy is improved for that specific camera, but the system becomes dependent on the training camera and accuracy decreases when using different lenses
Solution Approach 1:
The patent segments the depth estimation process into two independent components: (1) camera-specific feature extraction that learns from training data, and (2) lens-specific parameter adaptation that can be adjusted for different lenses. This allows the system to maintain accurate depth estimation across multiple camera types by separately optimizing for camera characteristics and lens characteristics.
Solution Approach 2:
The patent introduces lens-specific parameters (such as focal length, field of view, and distortion coefficients) as adjustable variables. By changing these parameters based on the specific lens being used, the system adapts the depth estimation algorithm to different lenses without requiring complete retraining, thus maintaining accuracy across various lens types.
2Adaptability or versatility
If training data is collected using multiple types of cameras and lenses, then camera-agnostic depth estimation is achieved, but training cost and time consumption increase significantly
Solution Approach 1:
The patent performs preliminary action by collecting training data from a single reference camera and pre-processing it into a standardized format. This preliminary training establishes a baseline depth estimation model that can then be quickly adapted to other cameras and lenses through parameter adjustment rather than complete retraining, significantly reducing the time and resources needed for multi-camera training.
Solution Approach 2:
The patent creates a virtual reference camera model that copies and standardizes features from various real cameras. Instead of collecting data from multiple physical cameras, the system uses computational modeling to simulate different camera characteristics, thereby achieving camera-agnostic training without the need for extensive physical data collection across multiple devices.
3Measurement precision
If extensive ERP images with ground-truth depth maps are collected for training, then accurate depth estimation for various lenses is achieved, but data collection cost and complexity increase
Solution Approach 1:
The patent introduces an intermediary computational model that translates depth information from a reference camera format to target camera formats. Instead of directly collecting and processing ERP images for every camera type, this intermediary model performs the conversion and adaptation, simplifying the training data collection process while maintaining depth estimation accuracy across different lenses.
Solution Approach 2:
The patent creates synthetic ERP images with ground-truth depth maps by computationally projecting and transforming images from a reference camera. Rather than physically capturing ERP images for training, the system generates synthetic training data through image processing and geometric transformation, thereby avoiding the complexity of actual ERP image collection while providing sufficient training material.
Data Source
AI summary
A method of performing depth estimation for images includes, at one or more processing devices receiving an input image captured by a camera, converting the input image to an equirectangular (ERP) image in an ERP space, performing depth estimation for the ERP image by using an ERP depth model to determine respective distances of features in the ERP image from the camera and generate a depth estimation output based on the respective distances, and controlling one or more functions of a device based on the depth estimation output.