Neural Radiance Field Nadir Ray Mapping for Satellite Depth Extraction
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Solution Overview
Problem
Conventional 3D reconstruction processes for satellite images face challenges due to uniform colors, poor collection geometry, and limited imagery, leading to artifacts and inefficiencies in extracting depth information.
Innovation Solution
Utilizing neural radiance fields with a machine-learning model to map nadir rays in a first space to a second space, enabling the rendering of depth and color information from satellite images, which includes using a Multilayer Perceptron network to approximate a continuous 3D scene representation and employing volume rendering techniques to create accurate Digital Surface Models (DSMs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional photogrammetry is used for 3D reconstruction from satellite images, then processing speed is fast, but measurement precision deteriorates due to artifacts and limited imagery from uniform colors, poor collection geometry, and transient objects
Solution Approach 1:
The patent replaces conventional photogrammetry (mechanical/geometric approach) with neural radiance fields (machine learning-based approach). The neural network model learns to map satellite images to 3D depth information, substituting traditional geometric reconstruction methods with a data-driven neural network that can handle uniform colors, poor collection geometry, and transient objects more effectively.
Solution Approach 2:
The patent changes the fundamental parameters of the reconstruction approach by using neural networks to model the relationship between 2D satellite images and 3D depth. Instead of relying on traditional photogrammetric parameters and geometric constraints, the system uses learned neural network parameters that can adapt to various imaging conditions and produce more accurate depth maps.
2Measurement precision
If neural radiance fields are used to improve depth extraction accuracy, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model on a large dataset of satellite images and their corresponding ground truth depth maps. This pre-training phase allows the model to learn optimal feature representations and depth mapping relationships in advance, so that during actual processing, the model can quickly infer depth from new satellite images without requiring time-consuming computations.
Solution Approach 2:
The patent uses copying by creating a neural network model that copies and generalizes the relationship between 2D satellite images and 3D depth from training data. The trained model can then replicate this mapping relationship for new images, providing fast and accurate depth extraction without re-computing from scratch for each new input.
3Adaptability or versatility
If traditional photogrammetry is used, then device complexity is low, but adaptability deteriorates due to inability to handle uniform colors, poor collection geometry, and transient objects
Solution Approach 1:
The patent changes the fundamental parameters of the reconstruction approach by using neural networks to model the relationship between 2D satellite images and 3D depth. Instead of relying on traditional photogrammetric parameters and geometric constraints, the system uses learned neural network parameters that can adapt to various imaging conditions and produce more accurate depth maps.
Solution Approach 2:
The patent applies universality by training the neural network model to handle multiple challenging imaging conditions simultaneously - uniform colors, poor collection geometry, and transient objects - within a single unified model. This multi-functional approach allows the system to adapt to various difficult scenarios that would require separate specialized methods in traditional photogrammetry.
Data Source
AI summary
Extraction of depth from satellite images using neural radiance fields may be provided. Satellite image data may be received. Then a plurality of nadir rays, each with a plurality of points, in a first space may be constructed. A Machine Learning (ML) model may then be used to map the plurality of points along the plurality of nadir rays in the first space to a second space. Then the plurality of points along the plurality of nadir rays in the second space may be rendered into images and depths.