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Found 4 papers, 3 papers with code
Editing 3D Scenes via Text Prompts without Retraining
To tackle these issues, we introduce a text-driven editing method, termed DN2N, which allows for the direct acquisition of a NeRF model with universal editing capabilities, eliminating the requirement for retraining.
PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures
To address this limitation and maximize the potential of each architecture, we propose Progressive Volume Distillation with Active Learning (PVD-AL), a systematic distillation method that enables any-to-any conversions between different architectures.
One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation
In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions.
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Arch-Net: Model Distillation for Architecture Agnostic Model Deployment
For example, Transformer Networks do not have native support on many popular chips, and hence are difficult to deploy.
