Image View Synthesis for Driving Simulators
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Solution Overview
Problem
Existing driving simulators for autonomous vehicles are time-consuming and exhaustive to build, and 3D reconstruction techniques used in them are not robust enough for environments with many dynamic objects.
Innovation Solution
A system that generates a requested image view using map information, contextual information, and queries specifying position and direction of view, employing physics-based metrics and auto-encoders to create a realistic driving simulation with dynamic objects without extensive environment description or assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 3D reconstruction techniques (SLAM, SfM, neural networks) are used to build driving simulators, then the simulator can handle dynamic objects, but the building process becomes time-consuming and exhaustive
Solution Approach 1:
The patent uses image translation and synthesis to create virtual views by copying and transforming existing image data rather than performing exhaustive 3D reconstruction. The system translates images from one viewpoint to another viewpoint using learned transformations, effectively copying visual information across different perspectives without reconstructing the entire 3D environment.
Solution Approach 2:
The system changes the parameter space by working directly in image space rather than 3D space. Instead of reconstructing 3D models and then rendering views, the patent transforms images by adjusting parameters like viewpoint position, camera intrinsics, and image coordinates directly, achieving view synthesis through parameter transformations in the 2D image domain.
2Manufacturing precision
If extensive environment description and assumptions are provided, then the simulator can be built accurately, but the complexity and time required increase significantly
Solution Approach 1:
The system performs self-service by automatically learning the environment structure and relationships from existing images without requiring extensive manual environment descriptions or assumptions. The translation model learns scene geometry, camera positions, and object relationships implicitly from image data, eliminating the need for explicit environment modeling.
Solution Approach 2:
The patent replaces the mechanical 3D reconstruction process with a learned image transformation system. Instead of using traditional computer vision algorithms to reconstruct 3D models from multiple views, the system uses a neural network to directly translate images between viewpoints, substituting complex mechanical reconstruction processes with a learned mapping function.
Data Source
AI summary
Systems and methods for generating a requested image view are disclosed. Exemplary implementations may: electronically store map information and contextual information for an area; receive a query for the requested image view; determine, based on the parameter values specified by the query and the map information, values of the physics-based metric; translate the contextual information to a translated representation of the contextual information; encode, based on the translated representation of the contextual information and the values of the physics-based metric, an image file that defines the requested image view such that the translated representation of the contextual information and the values of the physics-based metric are combined; and generate the requested image view by decoding the image file.