Monocular Camera 3D Reconstruction via Machine Learning Depth Estimation
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Solution Overview
Problem
Current image processing technologies, such as SLAM using a monocular camera, struggle to increase the amount of information in the observation space effectively, while methods using an RGB-D camera complicate the configuration and require additional hardware.
Innovation Solution
An image processing system that integrates three-dimensional coordinates of feature point clouds with depth and normal images estimated using machine learning, without the need for a depth camera, to enhance the information density in the observation space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a depth camera is used to measure surface depth and express three-dimensional shape, then the amount of information on the observation space is increased, but the configuration becomes complicated
Solution Approach 1:
The patent creates a virtual depth camera through machine learning that copies the function of a physical depth camera. The neural network model processes images from the monocular camera to generate depth information, effectively creating a software-based copy of depth sensing capability without requiring actual depth camera hardware.
Solution Approach 2:
The patent replaces the mechanical/optical system of a physical depth camera with a computational system based on machine learning. Instead of using optical sensors and depth measurement hardware, the system uses a neural network that processes 2D images to predict 3D depth information, substituting physical measurement with computational inference.
2Device complexity
If only feature point cloud coordinates are extracted from taken images, then the configuration remains simple, but the amount of information on the observation space cannot be sufficiently increased
Solution Approach 1:
The patent transforms the parameter representation from simple 2D feature point coordinates to enriched 3D point cloud data with depth and normal information. By changing the parameter dimensionality and adding depth components through machine learning, the system increases information content while maintaining the simplicity of using only a monocular camera.
Solution Approach 2:
The patent adds the depth dimension to the 2D image data by using a neural network to predict 3D coordinates from 2D images. This dimensionality transformation converts planar image information into volumetric spatial information, enabling 3D reconstruction without additional depth-sensing hardware.
3Loss of information
If machine learning is used to estimate depth and normal images from taken images, then information density in observation space is increased without additional sensors, but processing complexity increases
Solution Approach 1:
The patent performs preliminary training of the neural network model using paired 2D images and 3D depth data before actual use. This preliminary action creates a pre-trained model that can quickly infer depth and normal information during runtime, reducing the computational burden during actual observation and reconstruction tasks.
Solution Approach 2:
The patent introduces a neural network model as an intermediary between the monocular camera and the 3D reconstruction process. This intermediary processes the 2D image data and generates predicted depth and normal maps, acting as a computational bridge that translates 2D visual information into 3D spatial understanding without requiring direct 3D sensing hardware.
Data Source
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AI summary
To simplify a configuration for increasing an amount of information on an observation space, taken image acquisition means (101) of an image processing system (10) acquires taken images that have been taken by image taking means (18), which is movable in a real space. Observation space information acquisition means (102) acquires, based on changes in position of a feature point cloud in the taken images, observation space information including three-dimensional coordinates of the feature point cloud in an observation space. Machine learning means (103) acquires, based on machine learning data on a feature of an object, additional information on a feature of a photographed object shown in the taken images. Integration means (104) integrates the observation space information and the additional information.