HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
This addresses the computational inefficiency of training individual NeRFs for each object, benefiting 3D vision applications, though it appears incremental as it builds on existing hypernetwork and NeRF paradigms.
The paper tackles the limited generalization of Neural Radiance Fields (NeRFs) by proposing a hypernetwork-based few-shot learning approach that generates 3D object representations from a small number of images without gradient optimization during inference, achieving high-quality results as confirmed by comparisons with state-of-the-art solutions.
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization properties, which means that we need to use significant computational resources to train individual architectures for each item we want to represent. To address this issue, we propose a few-shot learning approach based on the hypernetwork paradigm that does not require gradient optimization during inference. The hypernetwork gathers information from the training data and generates an update for universal weights. As a result, we have developed an efficient method for generating a high-quality 3D object representation from a small number of images in a single step. This has been confirmed by direct comparison with the state-of-the-art solutions and a comprehensive ablation study.