Surround View Artifact Reduction via ML
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Solution Overview
Problem
Camera systems used in vehicles often produce surround views with artifacts such as distortions, scale inconsistencies, and ghosting, which are undesirable in providing a clear and accurate view of the environment.
Innovation Solution
A method and system that utilize a machine learning model, trained with photorealistic scenes generated using deep learning and style transfer methods, to process camera data from multiple cameras, determining depth values and occlusions, and generating a surround view that approximates a ground truth view by simulating camera inputs and projecting them onto desired viewpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional camera systems are used to generate surround views, then the system complexity is low, but artifacts such as distortions, scale inconsistencies, and ghosting appear in the output
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the camera system and the surround view output. The model processes raw camera images and generates artifact-free surround views by learning the mapping from multiple camera inputs to the desired surround view output, thereby eliminating distortions, scale inconsistencies, and ghosting without requiring complex hardware modifications
Solution Approach 2:
The patent replaces traditional mechanical/optical image processing methods with a data-driven machine learning approach. Instead of using complex optical systems or manual calibration procedures to achieve accurate surround views, the system uses a trained neural network that learns the correct transformations from training data, substituting physical complexity with computational intelligence
2Manufacturing precision
If machine learning models are trained to eliminate artifacts in surround views, then the output quality improves, but the training and processing time increases
Solution Approach 1:
The machine learning model is trained in advance using photorealistic training scenes before deployment. This preliminary training phase allows the model to learn the complex mappings and artifact elimination strategies beforehand, so that during actual operation, the model can quickly process camera inputs without requiring real-time iterative optimization, thus reducing operational processing time
3Measurement precision
If photorealistic training scenes are generated using deep learning and style transfer methods, then the training data quality improves, but the data processing complexity increases
Solution Approach 1:
The patent generates photorealistic training scenes by copying and transforming real-world images through style transfer and synthetic generation methods. These synthesized training scenes replicate the appearance and characteristics of real camera inputs while providing ground truth surround views, enabling the model to learn from diverse, high-quality training data without requiring extensive manual annotation or real-world data collection
Data Source
AI summary
In various embodiments, methods and systems are provided for processing camera data from a camera system associated with a vehicle. In one embodiment, a method includes: storing a plurality of photorealistic scenes of an environment; training, by a processor, a machine learning model to produce a surround view approximating a ground truth surround view using the plurality of photorealistic scenes as training data; and processing, by a processor, the camera data from the camera system associated with the vehicle based on the trained machine learning model to produce a surround view of an environment of the vehicle.