InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules

Published: 16 Jan 2024, Last Modified: 05 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Neural Radiance Fields, Hypernetwork, Neural Rendering, Generalizability
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TL;DR: We present InsertNeRF, a novel paradigm that instills generalizability into NeRF and its derivative works.
Abstract: Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce **InsertNeRF**, a method for **INS**tilling g**E**ne**R**alizabili**T**y into **NeRF**. By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations. This novel design allows for more accurate and efficient representations of complex appearances and geometries. Experiments show that this method not only achieves superior generalization performance but also provides a flexible pathway for integration with other NeRF-like systems, even in sparse input settings. Code will be available at:
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Primary Area: representation learning for computer vision, audio, language, and other modalities
Submission Number: 693