Neural Radiance Field Continual Learning via Generative Replay
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Solution Overview
Problem
Conventional Neural Radiance Fields (NeRF) solutions face challenges in continual learning, including information loss ('forgetting') and slow operation, especially when handling scenes with changing appearance and geometry, and require significant storage for historical images.
Innovation Solution
The technology employs generative replay with Instant-NGP architecture and trainable embeddings, allowing the model to adapt to scene changes without storing historical images, using segmentation masks to handle transient objects and maintaining rendering quality close to the upper bound model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional NeRF solutions are used for continual learning, then the model can learn from new scans, but information loss (forgetting) occurs and operation becomes slow
Solution Approach 1:
The patent applies preliminary action by pre-processing multi-view images into intermediate representations (such as depth maps, normal maps, or feature embeddings) before the main NeRF training process. These intermediate representations are stored and reused during continual learning, allowing the model to adapt to new scans without reprocessing原始 images, thereby preventing information loss while maintaining operational efficiency
Solution Approach 2:
The patent creates copies of essential scene information in the form of intermediate representations that can be stored efficiently. Instead of storing all original multi-view images, the system creates and stores compressed intermediate representations that capture key geometric and appearance features, enabling the model to recall and learn from past scenes without forgetting
2Adaptability or versatility
If conventional NeRF solutions are used for continual learning, then the model can adapt to new scenes, but operation speed becomes slow
Solution Approach 1:
The patent performs preliminary processing of multi-view images into intermediate representations before main training. These pre-processed representations (depth maps, normal maps, feature embeddings) are stored and directly reused during continual learning operations, eliminating the need to reprocess原始 images and significantly speeding up adaptation to new scenes
Solution Approach 2:
The patent segments the complex NeRF training process into distinct stages: initial pre-processing of multi-view images into intermediate representations, followed by efficient fine-tuning on new scans using these representations. This segmentation allows the computationally intensive processing to be done once, with rapid updates possible thereafter
3Reliability
If historical images are stored for continual learning, then rendering quality is maintained, but storage requirements increase significantly
Solution Approach 1:
The patent creates compact copies of scene information in the form of intermediate representations (depth maps, normal maps, feature embeddings) that are much smaller than original multi-view images. These compressed representations capture essential geometric and appearance features, maintaining rendering quality while dramatically reducing storage requirements
Solution Approach 2:
The patent transforms multi-view images into different parameter spaces through intermediate representations. By encoding scene geometry and appearance in compressed parameter forms (such as depth values, surface normals, or learned feature vectors), the system maintains the information needed for high-quality rendering while using far less storage space than original images
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that trains a first neural radiance field (NeRF) model with first images corresponding to a first instance of a scene, detects second images corresponding to a second instance of the scene, and applies generative replay and the second images to the first NeRF model to obtain a second NeRF model, wherein one or more of appearance changes or geometry changes in the second instance of the scene relative to the first instance of the scene are incorporated into the second NeRF model.