Neural Network Scene Representation for Static Transient Object Separation
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Solution Overview
Problem
Current view synthesis techniques face challenges in accurately rendering scenes from arbitrary camera views, particularly in distinguishing and separating static and transient objects, which affects the photorealism and accuracy of generated images.
Innovation Solution
A neural network model is trained to generate prediction information for 3D points on camera rays, sampling target points based on object density and probability of being transient, and projecting pixel values to render images, while distinguishing between static and transient objects to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If view synthesis is performed using traditional rendering methods, then the process is computationally efficient, but the ability to distinguish and separate static and transient objects is poor
Solution Approach 1:
The patent segments the scene representation by introducing separate latent vectors for static objects (l_static) and transient objects (l_transient), allowing the neural network to process and distinguish between these two object types independently. This segmentation enables accurate separation of static and transient objects while maintaining computational efficiency through targeted processing of each object category.
2Productivity
If all 3D points on camera rays are processed to generate prediction information, then complete scene coverage is achieved, but computational resources are wasted on transient objects
Solution Approach 1:
The patent applies local quality by processing 3D points differently based on their object type. Static object points receive full processing with appearance information from l_static, while transient object points use l_transient. This localized differentiation ensures accurate scene representation for each object type while optimizing computational resources by avoiding unnecessary processing of transient objects with static object models.
3Reliability
If a single appearance model is used for all objects, then the system is simpler to implement, but photorealism is reduced due to inability to capture transient object characteristics
Solution Approach 1:
The patent introduces dynamics by making the appearance model adaptive to object type. The system dynamically selects between l_static and l_transient based on the transient object probability β, allowing the appearance characteristics to change according to the object being rendered. This dynamic approach significantly improves photorealism by capturing the distinct visual properties of transient objects like people and vehicles, while the probabilistic switching mechanism keeps the system implementation manageable.
Data Source
AI summary
An image processing method and an image processing apparatus are provided. The image processing method includes: receiving a camera pose of a camera corresponding to a target scene; generating a piece of prediction information including either a color of an object included in the target scene or a density of the object, wherein the prediction information is generated by applying, to a neural network model, three-dimensional (3D) points on a camera ray formed based on the camera pose; sampling, among the 3D points, target points corresponding to a static object, wherein the sampling is based on the piece of prediction information; and outputting a rendered image corresponding to the target scene by projecting a pixel value corresponding to the target points onto the target scene and rendering the target scene onto which the pixel value may be projected.