Generalizable NGP-SR: Generalizable Neural Radiance Fields Super-Resolution via Neural Graph Primitives

Wanqi Yuan; Omkar Sharad Mayekar; Connor Pennington; Nianyi Li

cs.CVMar 20, 2026

Generalizable NGP-SR: Generalizable Neural Radiance Fields Super-Resolution via Neural Graph Primitives

Authors: Wanqi Yuan, Omkar Sharad Mayekar, Connor Pennington, Nianyi Li

Organizations: Clemson University

Abstract

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, naively super-resolving per-view renderings in 2D often breaks multi-view consistency. We propose Generalizable NGP-SR, a 3D-aware super-resolution framework that reconstructs an HR radiance field directly from low-resolution (LR) posed images. Built on Neural Graphics Primitives (NGP), NGP-SR conditions radiance prediction on 3D coordinates and learned local texture tokens, enabling recovery of high-frequency details within the radiance field and producing view-consistent HR novel views without external HR references or post-hoc 2D upsampling. Importantly, our model is generalizable: once trained, it can be applied to unseen scenes and rendered from novel viewpoints without per-scene optimization. Experiments on multiple datasets show that NGP-SR consistently improves both reconstruction quality and runtime efficiency over prior NeRF-based super-resolution methods, offering a practical solution for scalable high-resolution novel view synthesis.

Figures & tables

Figure 1: NGP-SR takes low-resolution (LR) images with camera poses and encodes them into hierarchical view-texture tokens that capture local spatial and pose-aware texture cues. These tokens are interpolated through a unified hash table with adaptive, camera-conditioned weights to produce robust multi-resolution feature vectors. Finally, a global feature fusion module integrates holistic LR image context, and a camera-aware decoder synthesizes high-resolution, view-consistent outputs.

Figure 2: 2D illustration of Cross-Level Hash Mapping mechanism. For each query token, hyper-vertices are identified at multiple resolution levels of the grid. Texture tokens introduce patch-dependent offsets, such that weight of the selected hyper-vertices may differ across resolutions for the same spatial coordinate. To reduce memory consumption, hyper-vertices from coarser levels are mapped to vertices at the finest resolution level.

Methods	Blender $\times$ 2 (100 $\times$ 100)			Blender $\times$ 4 (100 $\times$ 100)			Blender $\times$ 2 (200 $\times$ 200)			Blender $\times$ 4 (200 $\times$ 200)
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
TensoRF [ 4 ]	25.41	0.882	0.161	24.06	0.843	0.188	27.27	0.909	0.199	26.18	0.882	0.146
Instant-NGP [ 26 ]	26.34	0.916	0.157	24.83	0.872	0.229	28.29	0.934	0.109	27.07	0.904	0.159
3DGS [ 16 ]	27.57	0.935	0.124	25.59	0.887	0.213	29.49	0.949	0.087	27.86	0.916	0.147
LR+Bicubic	28.20	0.946	0.099	25.99	0.896	0.192	30.53	0.962	0.066	28.60	0.927	0.128
NeRF-SR [ 33 ]	28.74	0.924	0.066	28.07	0.921	0.071	30.82	0.953	0.055	28.46	0.921	0.076

Table 1: Comparison of SOTAs on Blender datasets with varying resolutions and scaling factors. The best results are highlighted in bold, and the second-best results are underlined.

Methods	LLFF $\times$ 2 (100 $\times$ 100)			LLFF $\times$ 4 (100 $\times$ 100)			LLFF $\times$ 2 (200 $\times$ 200)			LLFF $\times$ 4 (200 $\times$ 200)
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
TensoRF [ 4 ]	24.07	0.754	0.314	22.16	0.591	0.596	24.69	0.750	0.309	23.49	0.648	0.503
Instant-NGP [ 26 ]	22.17	0.667	0.335	20.90	0.543	0.479	21.47	0.616	0.332	20.81	0.571	0.428
3DGS [ 16 ]	23.20	0.735	0.307	21.52	0.581	0.580	23.76	0.748	0.294	22.73	0.654	0.478
LR+Bicubic	24.56	0.765	0.299	22.62	0.608	0.579	25.92	0.793	0.260	24.43	0.687	0.468
NeRF-SR [ 33 ]	26.24	0.838	0.123	24.07	0.713	0.252	27.15	0.837	0.122	25.44	0.745	0.208

Table 2: Comparison of SOTAs on LLFF datasets with varying resolutions and scaling factors. The best results are highlighted in bold, and the second-best results are underlined.

Figure 3: The qualitative comparison of our method on the Blender datasets, with input images at 200 $\times$ 200 resolution and $\times$ 4 super-resolution. The leftmost images show the low-resolution inputs, while the images highlighted in red correspond to our results. Compared with other methods that fail to recover fine structures, our approach successfully reconstructs sharp and detailed results. Notably, our method consistently enhances regions where other approaches produce blurred or low-frequency reconstructions.

Figure 4: The qualitative comparison of our method on the LLFF datasets, with input images at 200 $\times$ 200 resolution and $\times$ 4 super-resolution.The leftmost images show the low-resolution inputs, while the images highlighted in red correspond to our results. Our method successfully reconstructs fine details in leaves and architectural structures, and consistently improves reconstruction quality in regions where other methods exhibit degraded results.

Figure 5: Quantitative ablation on the Lego scene.

Methods	DTU
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
MVSGaussian	21.95	0.863	0.112
GeFU	23.15	0.880	0.100
GDB-NeRF	22.42	0.867	0.111
Ours ( $\times$ 4)	31.38	0.884	0.122

Table 3: Comparison of SOTAs on DTU datasets with

512{\times}512

resolution.

Figure 6: Qualitative evaluation of novel-scene generalization on the DTU dataset. Each panel shows the reconstructed image (left) and two zoomed-in crops (right). Baselines use $512{\times}512$ inputs, while our method operates on $128{\times}128$ inputs with $\times 4$ super-resolution and still reconstructs sharp details with accurate lighting.

Methods	Lego $\times$ 2 (100 $\times$ 100)
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Ours w/o $\mathcal{G}_{\text{cam}}$	26.69	0.892	0.082
Ours w/o $\mathcal{G}_{\text{CNN}}$	13.38	0.538	0.471
Ours w/o $\mathcal{G}_{\text{fuse}}$	13.69	0.585	0.436
Ours	30.60	0.952	0.075

Table 4: Ablation studies of our method on the Lego dataset using PSNR, SSIM, and LPIPS metrics.

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Abstract

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