Spatial Estimation Model for Neural Radiance Field Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing LiDAR and NeRF techniques do not efficiently utilize spatial information, as they compress distance information into zero-dimensional data during rendering, limiting the effective use of abundant information from sensors like cameras and LiDARs.
Innovation Solution
An arithmetic operation system that acquires spatial distribution signals, samples points along emission waves, estimates densities, forms comparison signals, calculates differences, and updates spatial estimation models to improve training efficiency and reduce the number of sensors required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If line-integration of density distribution is performed during rendering, then a three-dimensional structure can be reconstructed, but information in the distance direction is compressed into zero-dimensional information, reducing training efficiency
Solution Approach 1:
The patent transforms the rendering process from compressing 3D spatial information into 2D image data, then back to 3D, by directly operating on 3D density distributions in the neural radiance field. This dimensional preservation allows the model to learn spatial structures more efficiently without information loss, while still achieving accurate 3D reconstruction.
2Measurement precision
If multiple sensors are used to acquire spatial information, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent enables a single sensor to effectively provide multi-dimensional spatial information by having the neural radiance field model learn to represent and utilize density distributions along ray paths. The model itself compensates for the limited information from a single sensor by inferring 3D spatial structures from 2D observations, eliminating the need for multiple physical sensors.
3Device complexity
If abundant spatial information from sensors is compressed during rendering, then a simplified representation is achieved, but the useful information cannot be effectively utilized
Solution Approach 1:
The patent changes the representation parameters from compressed 2D image data to 3D density distributions defined by position coordinates (x, y, z) and density values. This parameter transformation allows the model to work directly with uncompressed spatial information, preserving all useful data while maintaining a manageable representation through the neural network's latent space.
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 is a spatial distribution signal observed 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. In addition, the estimated signal is a signal for comparing with the teaching signal, and is an estimated spatial distribution signal. The estimated signal is formed based on estimated density associated to each sample point acquired from a spatial estimation model, by a sampling unit inputting information about a position of each of a plurality of sample points on the path to the spatial estimation model. An updating unit updates the spatial estimation model, based on the difference amount.