Spatial Estimation Model Training via Wavefront Plane Integration
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Solution Overview
Problem
Existing LiDAR and NeRF techniques inefficiently utilize spatial information by compressing distance direction data into zero-dimensional information, limiting the effective use of abundant data from sensors like cameras and LiDARs.
Innovation Solution
An arithmetic operation system that acquires spatial distribution signals from multiple sensors, samples points along emission waves, and updates a spatial estimation model based on differences between teaching and estimated signals, allowing for more efficient training and utilization of multi-dimensional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If line-integration of density distribution is performed on ray path to render pixel value, then rendering can be achieved, but information in distance direction is compressed into zero-dimensional information, causing loss of spatial information
Solution Approach 1:
The patent transforms the rendering process from line-integration along ray paths to plane-integration over wavefront surfaces. This dimensional change from 1D line integration to 2D plane integration preserves spatial information in the distance direction by maintaining it as one-dimensional distribution data rather than compressing it to zero-dimensional pixel values. The wavefront representation adds a spatial dimension back to the rendered output.
Solution Approach 2:
The patent creates a copy of the spatial information by generating both pixel values and corresponding distance distribution data during the rendering process. Instead of losing distance information during rendering, the system copies this information into a separate distance map that preserves the one-dimensional distance distribution, allowing both visual rendering and spatial analysis to coexist.
2Measurement precision
If multiple sensors are used to acquire spatial information from multiple viewpoints, then spatial estimation accuracy is improved, but the number of sensors and calculation time increase
Solution Approach 1:
The patent makes the rendering system multi-functional by enabling it to simultaneously produce both traditional pixel-based images and distance distribution maps from the same wavefront data. This universal approach allows a single sensor system to achieve multiple objectives (visual rendering and spatial measurement) without requiring separate processing pipelines, thereby improving calculation efficiency while maintaining spatial estimation accuracy.
Solution Approach 2:
The patent merges the generation of pixel values and distance distribution information into a single unified rendering process. Instead of separately processing data for visual output and spatial analysis, the system combines both functions into one wavefront-based rendering operation, reducing redundant calculations and improving overall productivity while maintaining high measurement precision.
Data Source
AI summary
In an arithmetic operation system, an updating unit updates a spatial estimation model, based on a first difference amount and a second difference amount. The first difference amount is a difference amount between a first teaching signal and a first estimated signal. The first teaching signal is a spatial distribution signal observed by a first sensor with respect to a spatial structure on a path of an emission wave in a target space (i.e., a teaching space) by using the emission wave. The second difference amount is a difference amount between a second teaching signal and a second estimated signal. The second teaching signal is an observed signal observed by a second sensor different in type from the first sensor. The second estimated signal is a signal for comparing with the second teaching signal, and is a signal of a form similar to that of the second teaching signal.