Magnituder Layers for Implicit Neural Representations in 3D
Keywords: NeRF, SDF, Implicit Representations
TL;DR: We introduce novel neural network layers called "magnituders" that can reduce the time complexity of training and inference speeds of NeRF and iSDF models.
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.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 10451
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