Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion Strategies
We present a novel method for diffusion-guided frameworks for view-consistent super-resolution (SR) in neural rendering. Our approach leverages existing 2D SR models in conjunction with advanced techniques such as Variational Score Distilling (VSD) and a LoRA fine-tuning helper, with spatial training to significantly boost the quality and consistency of upscaled 2D images compared to the previous methods in the literature, such as Renoised Score Distillation (RSD) proposed in DiSR-NeRF (1), or SDS proposed in DreamFusion. The VSD score facilitates precise fine-tuning of SR models, resulting in high-quality, view-consistent images. To address the common challenge of inconsistencies among independent SR 2D images, we integrate Iterative 3D Synchronization (I3DS) from the DiSR-NeRF framework. Our quantitative benchmarks and qualitative results on the LLFF dataset demonstrate the superior performance of our system compared to existing methods such as DiSR-NeRF.
Code (2)
Tasks
NeRFNeural RenderingSuper-ResolutionSimilar Papers 제목 키워드 기반
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis
Novel View Synthesis (NVS) aims at synthesizing an image from an arbitrary viewpoint using multi-view images and camera poses. Among the methods for NVS, Neural Radiance Fields (NeRF) is capable of NVS for an arbitra…
Image Super-ResolutionNeRFNovel View SynthesisSuper-ResolutionGeneralizable NGP-SR: Generalizable Neural Radiance Fields Super-Resolution via Neural Graph Primitives
Neural Radiance Fields (NeRF) achieve photorealistic novel view synthesis but become costly when high-resolution (HR) rendering is required, as HR outputs demand dense sampling and higher-capacity models. Moreover, naive…
Novel View SynthesisZS-SRT: An Efficient Zero-Shot Super-Resolution Training Method for Neural Radiance Fields
Neural Radiance Fields (NeRF) have achieved great success in the task of synthesizing novel views that preserve the same resolution as the training views. However, it is challenging for NeRF to synthesize high-quality hi…
Inverse RenderingNeRFSuper-ResolutionAddressing the Shape-Radiance Ambiguity in View-Dependent Radiance Fields
We present a method for handling view-dependent information in radiance fields to help with convergence and quality of 3D reconstruction. Radiance fields with view-dependence suffers from the so called shape-radiance amb…
3D ReconstructionPix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation
We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as training NeRF requires multiple views o…
3D-Aware Image SynthesisImage GenerationNeRFNovel View Synthesis+2