Spatial Estimation Model Training via Curved Surface Regions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current LiDAR and NeRF techniques inefficiently utilize spatial information, compressing distance information into zero-dimensional data, leading to suboptimal spatial training and underutilization of sensor data.
Innovation Solution
An arithmetic operation system and training method that acquire a spatial distribution signal using an emission wave across a region of interest, inputting sample point positions to a spatial estimation model, calculating estimated densities, and updating the model based on differences between teaching and estimated signals, allowing for more efficient utilization of sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If line-integrating density distribution on a ray path during rendering, then rendering process can be completed, but spatial information is compressed into zero-dimensional data losing distance direction information
Solution Approach 1:
The patent introduces a curved surface region as a new dimensional framework for sampling points, transitioning from traditional one-dimensional ray paths to two-dimensional curved surfaces. This allows preserving spatial distribution information across multiple dimensions while maintaining rendering efficiency. The curved surface region intersects multiple emission reference directions, enabling comprehensive spatial information capture without compression.
2Reliability
If using traditional NeRF training method, then three-dimensional structure can be trained, but training cost and calculation time are high
Solution Approach 1:
The patent segments the emission wave region into multiple curved surface regions, each intersecting multiple emission reference directions. By dividing the complex training task into manageable segments (curved surface regions with sample points), the system reduces computational complexity while maintaining training reliability. This segmentation allows parallel processing and reduces overall training time.
Solution Approach 2:
The patent changes the parameter space from traditional ray-based one-dimensional integration to curved surface-based multi-dimensional sampling. By transforming the mathematical representation of emission paths from straight rays to curved surfaces intersecting multiple directions, the system achieves more efficient spatial information utilization and reduces training computational burden.
3Measurement precision
If using traditional LiDAR and NeRF combination, then spatial structure can be estimated, but sensor data is not fully utilized
Solution Approach 1:
The patent creates a universal framework where curved surface regions can accommodate multiple emission reference directions and sensor types. This multi-functional approach allows the system to process and utilize spatial distribution signals from various sensors (LiDAR, cameras) within a unified training paradigm, maximizing sensor data utilization while maintaining estimation precision.
Data Source
AI summary
In an arithmetic operation system, an evaluation unit calculates a difference amount between a teaching signal and an estimated signal. The teaching signal has a value that is obtained by integrating spatial distribution signals observed by a sensor using emission waves for a spatial structure along a region of interest in an emission wave region in which emission waves are emitted from a plurality of emission reference directions and reach the sensor. The region of interest is a curved line region or a curved surface region intersecting the plurality of emission reference directions. This estimated signal is calculated by integrating a plurality of pieces of estimated density of a plurality of sample points obtained from a spatial estimation model by having a sampling unit input information about a position of each of the plurality of sample points on the region of interest to the spatial estimation model.