NeRF 3D Model Creation Using Semantic Maps
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Solution Overview
Problem
Current 3D model creation techniques are time-consuming and resource-intensive, requiring precise camera location tracking and consuming significant power and processor resources.
Innovation Solution
A device and method utilizing a neural radiance field (NeRF) neural network and semantic maps to generate 3D models by accessing location data from cameras and determining angles for optimal image capture, allowing for efficient creation of 3D models with reduced power and processor usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional 3D model creation techniques are used, then model accuracy can be achieved, but the process is time-consuming and consumes significant power and processor resources
Solution Approach 1:
The system performs preliminary actions by pre-mapping the environment using LiDAR and cameras to create a semantic map with pre-identified objects and their locations. This preliminary mapping eliminates the need for real-time camera location tracking during 3D model creation, significantly reducing power consumption and processing requirements while maintaining model accuracy.
Solution Approach 2:
The patent introduces an intermediary semantic map that acts as a mediator between the physical environment and the NeRF processing. The semantic map contains pre-extracted object locations and camera positions, serving as an intermediary data structure that eliminates the need for continuous camera tracking and reduces the computational burden on the NeRF network during 3D model generation.
2Measurement precision
If precise camera location tracking is implemented, then 3D model accuracy is improved, but processor resources and time are excessively consumed
Solution Approach 1:
Camera locations and object positions are determined in advance during the semantic map creation phase using LiDAR and camera data. This preliminary determination of precise locations eliminates the need for continuous real-time tracking during 3D model creation, maintaining measurement precision while dramatically reducing the time required for model generation.
Solution Approach 2:
The system extracts camera location data and object position information from the semantic map created during preliminary mapping. By extracting this location information in advance and storing it in the semantic map, the system eliminates the need for continuous camera tracking operations during 3D model creation, reducing processing time while maintaining precision.
3Reliability
If real-time camera tracking is performed, then accurate 3D models can be generated, but power consumption and processor load increase significantly
Solution Approach 1:
The semantic map serves as an intermediary that pre-stores accurate camera locations and object positions determined during initial mapping. This intermediary data structure allows the NeRF network to generate accurate 3D models using pre-determined location data from the semantic map, eliminating the need for power-intensive real-time camera tracking while maintaining model reliability.
Solution Approach 2:
The system performs preliminary camera location determination and object identification during the semantic map creation phase. By completing these computationally intensive tasks in advance, the system maintains 3D model accuracy while significantly reducing processor power consumption during the actual 3D model generation process.
Data Source
AI summary
In one aspect, a device includes a processor assembly and storage accessible to the processor assembly. The storage includes instructions executable by the processor assembly to access a semantic map and receive input from at least a first camera indicated in the semantic map. The instructions are also executable to use location data for the first camera as indicated in the semantic map, the input, and a neural radiance field (NeRF) neural network to generate a three-dimensional (3D) model of at least one object indicated in the semantic map.