Neural Fields for Sparse View Synthesis of Outdoor Scenes
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Solution Overview
Problem
Current neural field techniques are limited in representing complex urban scenes and require multiple views for accurate 3D reconstruction and editing, making them inefficient for real-world unbounded outdoor scenarios where objects of interest are observed from few views.
Innovation Solution
The NeO 360 system employs an image-conditional triplanar representation to model 3D surroundings from sparse views, enabling few-shot novel view synthesis and rendering of 360° outdoor scenes using a hybrid local and global features representation, which can be queried from any world point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If per-scene optimization from large number of views is used, then manufacturing precision of 3D scene representation is improved, but device complexity and computational complexity increase
Solution Approach 1:
The system performs preliminary action by pre-training a generalizable neural field model on a large dataset of outdoor scenes before actual use. This pre-training establishes prior knowledge about outdoor scene structures, lighting conditions, and object appearances, allowing the model to make accurate predictions from limited input views without requiring extensive per-scene optimization computations.
Solution Approach 2:
The neural field model achieves universality by being trained on diverse outdoor scenes and becoming capable of handling multiple different scenes with a single model. The model can generalize across various outdoor environments, weather conditions, and scene compositions, eliminating the need for separate optimization processes for each individual scene while maintaining high representation accuracy.
2Manufacturing precision
If per-scene optimization from large number of views is used, then manufacturing precision of 3D scene representation is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary action by pre-training a generalizable neural field model on a large dataset of outdoor scenes before actual use. This pre-training establishes prior knowledge about outdoor scene structures, lighting conditions, and object appearances, allowing the model to make accurate predictions from limited input views without requiring extensive per-scene optimization computations.
Solution Approach 2:
The system changes the parameter of input view quantity from the traditional large number of views to just a few views. By leveraging the pre-trained model's learned priors, the system achieves accurate 3D scene representation with significantly fewer input images, thereby improving productivity while maintaining manufacturing precision.
3Device complexity
If object reconstructions from single-view RGB inputs are used, then device complexity is reduced, but measurement precision and reliability decrease due to error-compounding multi-stage pipelines
Solution Approach 1:
The system merges multiple processing stages into a single unified neural field model. Instead of using separate multi-stage pipelines for panoptic segmentation, 3D bounding box estimation, and scene reconstruction, the model performs all these functions simultaneously through a single end-to-end trainable network, eliminating error compounding between stages while maintaining low device complexity.
Solution Approach 2:
The system replaces the mechanical multi-stage pipeline with a data-driven neural field model. The model learns to perform segmentation, geometry estimation, and rendering jointly through training on outdoor scenes, substituting the sequential mechanical processing stages with a unified learned representation that avoids error propagation.
Data Source
AI summary
The present disclosure provides neural fields for sparse novel view synthesis of outdoor scenes. Given just a single or a few input images from a novel scene, the disclosed technology can render new 360° views of complex unbounded outdoor scenes. This can be achieved by constructing an image-conditional triplanar representation to model the 3D surrounding from various perspectives. The disclosed technology can generalize across novel scenes and viewpoints for complex 360° outdoor scenes.