1. Magnituder Layers for Implicit Neural Representations in 3D
- Author
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Kim, Sang Min, Kim, Byeongchan, Sehanobish, Arijit, Choromanski, Krzysztof, Shim, Dongseok, Dubey, Avinava, and Oh, Min-hwan
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Improving the efficiency and performance of implicit neural representations in 3D, particularly Neural Radiance Fields (NeRF) and Signed Distance Fields (SDF) is crucial for enabling their use in real-time applications. These models, while capable of generating photo-realistic novel views and detailed 3D reconstructions, often suffer from high computational costs and slow inference times. To address this, we introduce a novel neural network layer called the "magnituder", designed to reduce the number of training parameters in these models without sacrificing their expressive power. By integrating magnituders into standard feed-forward layer stacks, we achieve improved inference speed and adaptability. Furthermore, our approach enables a zero-shot performance boost in trained implicit neural representation models through layer-wise knowledge transfer without backpropagation, leading to more efficient scene reconstruction in dynamic environments.
- Published
- 2024