HDR-NSFF: High Dynamic Range Neural Scene Flow Fields

Shin Dong-Yeon; Kim Jun-Seong; Kwon Byung-Ki; Tae-Hyun Oh

cs.CVMar 9, 2026

HDR-NSFF: High Dynamic Range Neural Scene Flow Fields

Authors: Shin Dong-Yeon, Kim Jun-Seong, Kwon Byung-Ki, Tae-Hyun Oh

Organizations: School of Electrical Engineering, KAIST · Dept. of Electrical Engineering, POSTECH · Grad. School of Artificial Intelligence, POSTECH · School of Computing, KAIST

Abstract

Radiance of real-world scenes typically spans a much wider dynamic range than what standard cameras can capture. While conventional HDR methods merge alternating-exposure frames, these approaches are inherently constrained to 2D pixel-level alignment, often leading to ghosting artifacts and temporal inconsistency in dynamic scenes. To address these limitations, we present HDR-NSFF, a paradigm shift from 2D-based merging to 4D spatio-temporal modeling. Our framework reconstructs dynamic HDR radiance fields from alternating-exposure monocular videos by representing the scene as a continuous function of space and time, and is compatible with both neural radiance field and 4D Gaussian Splatting (4DGS) based dynamic representations. This unified end-to-end pipeline explicitly models HDR radiance, 3D scene flow, geometry, and tone-mapping, ensuring physical plausibility and global coherence. We further enhance robustness by (i) extending semantic-based optical flow with DINO features to achieve exposure-invariant motion estimation, and (ii) incorporating a generative prior as a regularizer to compensate for limited observation in monocular captures and saturation-induced information loss. To evaluate HDR space-time view synthesis, we present the first real-world HDR-GoPro dataset specifically designed for dynamic HDR scenes. Experiments demonstrate that HDR-NSFF recovers fine radiance details and coherent dynamics even under challenging exposure variations, thereby achieving state-of-the-art performance in novel space-time view synthesis. Project page: https://shin-dong-yeon.github.io/HDR-NSFF/

Figures & tables

Figure 1: High Dynamic Range Neural Scene Flow Fields (HDR-NSFF) reconstruct dynamic HDR radiance field from (a) alternating-exposure monocular videos. Our method enables the rendering of (b) HDR novel views across both spatial and temporal domains. Additionally, we can generate (c) novel LDR views along with their corresponding depth maps.

Figure 2: Comparison of HDR video reconstruction on training views. Given alternating-exposure video, HDR video reconstruction baselines, i.e . , LAN-HDR ( Chung and Cho, 2023 ) , HDRFlow ( Xu et al., 2024 ) , and NECHDR ( Cui et al., 2024 ) fail to produce consistent results, while our model ensures temporal coherence and recovers valid information in saturated regions.

Figure 3: Overall pipeline of our proposed method. HDR-NSFF takes an alternating-exposure monocular video as input and estimates 3D scene flow for the sampled points along each ray. Neighboring frames are then warped to render the HDR radiance at the target frame, which is tone-mapped to LDR via a white-balance and camera-response function module. Photometric loss with the ground-truth LDR images, along with optical flow and depth constraints from off-the-shelf models, jointly optimize both the scene flow fields and tone-mapping module in an end-to-end manner.

Figure 4: Visualization of flow estimation under varying exposure conditions. RAFT yields noticeable errors when exposures vary. Even with gamma correction, RAFT (Gamma) still fails to produce accurate results. While fine-tuning on synthetic data RAFT (Finetuned) shows moderate improvement, our proposed semantic-based approach achieves superior accuracy.

Figure 5: Generative prior (GP) pipeline for HDR dynamic radiance fields optimization. Unseen novel views are first rendered and then refined via a GP to restore details in regions with broken correspondences. These enhanced views serve as pseudo-labels for the progressive optimization of HDR dynamic radiance fields. This bootstrapping mechanism stabilizes correspondences, mitigating issues related to sparse viewpoints and saturated pixels in monocular videos with alternating exposures.

Figure 6: Evaluation setup and sampled sequences from our proposed GoPro dataset . To evaluate novel view synthesis, we use nine GoPro Hero 13 Black cameras synchronized to record multi-view video at three exposures (mid, low, high). We construct a alternating-exposure monocular video by selecting one frame per time step across exposures, and use the remaining views for evaluation.

Figure 7: Qualitative results of novel view synthesis on GoPro dataset. The odd-numbered rows show the LDR-rendered novel views along with their corresponding L1 error maps against the ground-truth novel view (leftmost). Our method consistently yields the smallest error across all scenes. The even-numbered rows present tone-mapped HDR novel views. Compared with HDR-HexPlane ( Wu et al., 2024a ) , our approach produces more accurate radiance, geometry, and motion representations.

Figure 9: Qualitative results on novel LDR/HDR view synthesis. We visualize LDR rendering results at varying exposure levels (low, mid, and high), tone-mapped HDR rendering by ours and corresponding ground-truth HDR references. We also visualize histograms of our HDR images and ground truth. For better visualization, we plot HDR histogram using smoothed KDE method.

	NYUv2			ScanNet
Methods	Original	+2EV	-2EV	Original	+2EV	-2EV
MiDaS	9.08	13.68	9.35	8.66	13.78	10.22
DPT	9.21	12.96	8.95	8.27	13.62	9.57
Marigold	5.81	11.26	6.66	7.24	14.26	8.33
Depth-Anything-V2	4.87	7.63	5.10	4.82	10.57	6.36

Table 4: Depth estimation results under exposure variance. We employ AbsRel as the evaluation metric.

	Full			Dynamic only
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
w/o Tone-mapping	17.79	0.7048	0.0705	15.59	0.5577	0.1339
Fix CRF	25.55	0.8391	0.0487	20.43	0.6904	0.0911
MLP CRF	28.76	0.8861	0.0394	21.48	0.7256	0.0776
Piecewise CRF	31.01	0.9301	0.0233	22.55	0.7714	0.0697

Table 5: Comparison of tone-mapping designs.

Appendix figures & tables25 assets

Supplementary material from the paper’s appendix.

Appendix

Name	Exp. Time [s]	# of frames
Big jump	$\frac{1}{960},\frac{1}{2880},\frac{1}{7680}$	324
Side walk	$\frac{1}{960},\frac{1}{2880},\frac{1}{7680}$	324
Jumping jack	$\frac{1}{720},\frac{1}{1920},\frac{1}{7680}$	324
Pointing walk	$\frac{1}{720},\frac{1}{1920},\frac{1}{7680}$	324
Tube toss	$\frac{1}{720},\frac{1}{1920},\frac{1}{7680}$	324
Bear	$\frac{1}{120},\frac{1}{480},\frac{1}{1920}$	324

Appendix

Table S1: Parameter settings for real dataset.

Figure S1: Illustration of two experimental settings. We illustrate two experimental settings described in Sec. 4.2 in manuscript: (a) Novel view synthesis (b) Novel view and time synthesis.

Figure S2: DINOv2 feature visualization under varying exposures. Despite large changes in brightness, DINOv2 embeddings remain consistent, showing robust clustering across different exposure levels.

Figure S3: Effect of generative prior (GP). (a) GP provides GP provides additional plausible views from unseen views, (b) leading to more consistent and sharper reconstructions.

	Big jump						Side walk
Method	Full			Dynamic only			Full			Dynamic only
Method	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS
NSFF	16.23	0.6268	0.194	13.75	0.4476	0.2755	16.62	0.6346	0.1874	14.1	0.5524	0.2139
4DGS	20.02	0.7283	0.1751	14.21	0.3724	0.319	18.46	0.702	0.1724	13.56	0.3927	0.254
MotionGS	11.4	0.1354	0.4549	9.42	0.0862	0.5628	13.58	0.2387	0.3692	8.79	0.1025	0.513
NeRF-WT	27.09	0.9051	0.0738	16.31	0.5023	0.2301	25.29	0.9061	0.0641	13.23	0.4243	0.2143

Appendix

Table S2: Quantitative results of novel view synthesis on GoPro dataset. The green and yellow colors stand for the best and the second best , respectively.

	Big jump						Side walk
Method	Full			Dynamic only			Full			Dynamic only
Method	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS
NSFF	17.09	0.6856	0.1858	15.05	0.4765	0.3175	17.48	0.7095	0.1621	15.49	0.609	0.2292
HDR-Hexplane	19.01	0.5076	0.2301	15.13	0.4166	0.2604	19.08	0.4914	0.2389	16.33	0.5778	0.1699
Ours	30.13	0.924	0.0662	22.09	0.756	0.1515	30.18	0.927	0.0564	21.03	0.7304	0.1413
	Jumping jack						Pointing walk

Appendix

Table S3: Quantitative results of novel time synthesis on real data. The green and yellow colors stand for the best and the second best , respectively.

Methods	Lego	Mutant	Standup	Jumping Jack
	synthetic dataset-Full
	PSNR
NSFF	15.45	16.97	13.47	15.53
NeRF-WT	29.55	33.06	32.55	29.25
HDR-Hexplane	28.58	30.88	30.83	29.50
Ours	34.64	36.13	35.80	33.72

Appendix

Table S4: Quantitative results of novel view and time synthesis on synthetic dataset. The green and yellow colors stand for the best and the second best , respectively.

	Full			Dynamic only
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Ours w/ RAFT ( Teed and Deng, 2020 )	30.42	0.9269	0.0246	21.38	0.7369	0.0675
Ours w/ Finetuned	30.68	0.9234	0.0253	21.51	0.7377	0.0689
Ours w/ Dino-Tracker ( Tumanyan et al., 2024 )	31.01	0.9301	0.0233	22.55	0.7714	0.0697

Appendix

Table S5: Ablation study of flow model. To compare the effect of flow regularization, we compare NVS performance of our approach against the baseline optical flow model (RAFT Teed and Deng (2020) ) and a stronger baseline fine-tuned RAFT on a multi-exposure adaptation of the FlyingThings3D dataset.

Figure S4: Qualitative results of MoSca with our proposed modules on GoPro outdoor scenes for HDR novel view synthesis. MoSca with Tone-mapping module (TM) produces HDR-like appearances but suffers from temporal inconsistency and distorted dynamic geometry due to exposure-sensitive motion estimation. In contrast, MoSca with TM and DINO-Tracker (DT) leverages exposure-robust semantic flow, yielding stable geometry and photometrically consistent HDR novel views under challenging exposure alternation.

	Full			Dynamic only
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
MoSca w/ TM	26.67	0.920	0.060	15.96	0.463	0.064
MoSca w/ TM + DT	29.35	0.937	0.040	21.22	0.751	0.064

Appendix

Table S6: Quantitative results of MoSca with our proposed modules on GoPro outdoor scenes for HDR novel view synthesis. MoSca with Tone-mapping (TM) aligns exposure-varied inputs into a consistent HDR radiance but still suffers from unreliable motion due to CoTracker’s exposure sensitivity. Incorporating DINO-Tracker (DT) provides exposure-robust semantic flow, significantly improving both HDR reconstruction and dynamic motion stability.

Figure S5: Qualitative comparison with 2D-to-4D HDR reconstruction. A two-stage baseline reconstructs HDR video using LAN-HDR ( Chung and Cho, 2023 ) , HDRFlow ( Xu et al., 2024 ) , or NECHDR ( Cui et al., 2024 ) , applies a fixed $\mu$ -law tone mapping, and performs 4D reconstruction with MoSca ( Lei et al., 2025 ) . For reference, we also include a MoSca-based variant of our method (HDR-MoSca). While 2D-to-4D baselines fail to recover, our approach yields temporally coherent HDR radiance and stable geometry.

Figure S6: Training view and ours reconstrcuted views on GoPro indoor dataset. The odd-numbered rows show sample LDR frames from the input sequence, captured with alternating exposures (low, high, and mid). The even-numbered rows present our tone-mapped HDR reconstruction results for the corresponding input views. Under- and over-exposed regions are reliably recovered across all scenes. Even in areas where severe saturation is expected, such as the smile-emoji picture on the wall or the region directly beneath the light source, our method accurately reconstructs fine details. Even the mid-exposure frames contain locally saturated regions, yet these areas are consistently restored with high fidelity.

Figure S7: Training view and ours reconstrcuted views on GoPro outdoor dataset. The odd-numbered rows show sample LDR frames from the input sequence, captured with alternating exposures (low, high, and mid). The even-numbered rows present our tone-mapped HDR reconstruction results for the corresponding input views. Across the outdoor scenes, our method robustly reconstructs HDR radiance even under strong and diverse motion patterns. It performs reliably in scenarios involving large vertical motion in Big Jump, lateral motion in Pointing Walk, and fast object motion in Tube Toss. Despite these challenging dynamics, the model successfully restores both saturated regions caused by strong sunlight reflections on buildings and under-exposed regions cast in shadow, demonstrating consistent reconstruction quality across a wide range of exposure conditions.

Figure S8: Qualitative results of novel view synthesis on Tumbler data.

Figure S9: Qualitative results of novel view synthesis on Bear thread data.

Figure S10: Qualitative results of novel view synthesis on Dog data.

Figure S11: Qualitative results of novel view synthesis on Big jump data.

Figure S12: Qualitative results of novel view synthesis on Jumping jack data.

Figure S13: Qualitative results of novel view synthesis on Pointing walk data.

Figure S14: Qualitative results of novel view synthesis on Side walk data.

Figure S15: Qualitative results of novel view synthesis on Tube toss data.

Figure S16: Qualitative results of novel view synthesis on Fire extinguisher data.

Figure S17: Qualitative results of novel view synthesis on Laptop data.

Figure S18: Qualitative results of novel view synthesis on Bag data.

Figure S19: Qualitative results of novel view synthesis on Ball data.

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