Neural Object Reconstruction for Safe Autonomous Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Training and testing autonomous systems in real-world environments is unsafe due to the potential for accidents caused by untrained virtual drivers, necessitating a safe and effective simulation method for evaluating and improving their performance.
Innovation Solution
A simulator is developed to create a virtual world that reconstructs real-world environments, using ray tracing and reflectance multilayer perceptron models to simulate sensor inputs, allowing for the training and testing of autonomous systems in a controlled and realistic manner, incorporating mixed-reality simulation and closed-loop evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous systems are trained and tested in real-world environments, then the training data is authentic and representative, but safety risks increase due to potential accidents from untrained virtual drivers
Solution Approach 1:
The patent creates virtual copies of real-world environments, objects, and scenarios through neural radiance field reconstruction. Instead of training directly in real-world environments, the system generates photorealistic virtual replicas that preserve the statistical properties and characteristics of real data while eliminating physical safety risks. This allows autonomous systems to be trained on authentic-looking data without exposing real-world participants to danger.
2Measurement precision
If virtual worlds replicate real-world objects accurately, then realism and diversity improve, but computational complexity and rendering time increase
Solution Approach 1:
The system performs preliminary reconstruction of real-world scenes into neural radiance field representations during an offline preprocessing stage. By pre-computing the complex neural network models that encode scene geometry, lighting, and appearance, the system transforms an otherwise computationally intensive real-time rendering problem into a more manageable query process. This preliminary action enables photorealistic rendering at lower runtime computational costs.
Solution Approach 2:
The patent implements progressive refinement of neural radiance fields, where rendering quality can be adjusted by controlling the number of sampling rays and refinement iterations. Instead of always computing maximum detail, the system adapts the level of computational effort to the specific requirements of each rendering task, providing a trade-off between realism and computational complexity based on application needs.
3Adaptability or versatility
If diverse real-world scenarios are simulated, then training coverage and robustness improve, but the time and resources required for simulation increase
Solution Approach 1:
The system pre-reconstructs multiple diverse real-world scenarios into neural radiance field representations during an offline stage, creating a library of photorealistic virtual environments. This preliminary capture and reconstruction of diverse scenarios allows the system to later rapidly instantiate and render from these pre-processed scenes without incurring the full computational cost of real-time reconstruction, thereby reducing simulation time while maintaining training coverage.
Data Source
AI summary
Real world object reconstruction and representation include performing operations that include sampling locations along a camera ray from a virtual camera to a target object to obtain a sample set of the locations along the camera ray. For each location of the at least a subset of the sample set, the operations include determining a position of the location with respect to the target object, executing, based on the position, a reflectance multilayer perceptron (MLP) model, to determine an albedo and material shininess for the location, and computing a radiance for the location and based on a viewing direction of the camera ray using the albedo and the material shininess. The operations further includes rendering a color value for the camera ray by compositing the radiance across the first sample set.