Neural View Synthesis via Sub-Region Rendering for Large Scenes
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Solution Overview
Problem
Existing neural image rendering techniques are limited to small-scale or object-centric reconstructions and struggle with large-scale environments due to artifacts, low visual fidelity, and challenges such as transient objects, model capacity limitations, and memory/compute constraints, making them unsuitable for applications like autonomous driving and aerial surveying.
Innovation Solution
The technique divides large environments into sub-regions, trains independent view synthesis models for each sub-region, and dynamically renders and combines them at inference time, incorporating appearance embeddings, learned pose refinement, and exposure conditioning to handle environmental changes and pose errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If existing neural image rendering techniques are applied to large environments, then reconstruction coverage is improved, but visual fidelity deteriorates due to artifacts and limited model capacity
Solution Approach 1:
The patent divides large-scale environments into multiple overlapping sub-regions, each processed by a dedicated view synthesis model. This segmentation allows each model to focus on a manageable portion of the scene, maintaining high visual fidelity while collectively covering large areas. The models are trained independently on data specific to their respective sub-regions, ensuring localized precision.
2Area of stationary object
If a single view synthesis model is trained on entire large environments, then comprehensive scene coverage is improved, but model capacity requirements increase beyond practical limits
Solution Approach 1:
The patent segments the large-scale environment into multiple sub-regions, each handled by a separate view synthesis model. This division reduces the model capacity requirements for each individual model, making them computationally feasible while collectively achieving comprehensive scene coverage through coordinated rendering of multiple models.
Solution Approach 2:
The patent introduces a spatial dimension to the system architecture by organizing multiple view synthesis models in a grid or hierarchical structure that corresponds to the spatial layout of the environment. This dimensional organization allows the system to scale to large environments without increasing the complexity of individual models, as each model operates within its designated spatial subset.
3Adaptability or versatility
If data from multiple data collection efforts are combined for large environments, then environmental variability is improved, but training data consistency deteriorates due to variance in geometry and appearance
Solution Approach 1:
The patent segments the training data processing by creating separate view synthesis models for different sub-regions, each trained on data from specific data collection efforts. This segmentation allows the system to incorporate diverse environmental variability across different regions while maintaining local data consistency within each model's training set, as each model learns from data collected under relatively consistent conditions for its specific sub-region.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for rendering a new image that depicts a scene from a perspective of a camera at a new camera viewpoint.