HDR-NSFF: High Dynamic Range Neural Scene Flow Fields
Authors: Shin Dong-Yeon
Kim Jun-Seong
Kwon Byung-Ki
Tae-Hyun Oh
Venue: ICLR 2026 Poster (2026)
Keywords: High Dynamic Range
Dynamic Radiance Fields
Scene Flow
Primary area: applications to computer vision, audio, language, and other modalities

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

Published as a conference paper at ICLR 2026
HDR-NSFF: HIGH DYNAMIC RANGE NEURAL SCENE FLOW FIELDS
Shin Dong-Yeon'
Kim Jun-Seong"
Kwon Byung-Ki
Tae-Hyun Oh
School of Electrical Engineering, KAIST Dept. of Electrical Engineering, POSTECH GGrad. School of Artificial Intelligence, POSTECH
Schooo of Computing, KAIST
Time
(b) Novel HDR space-time view synthesis
(a) Input: Aternating-exposii monocular videos
Mid
High
Low
Depth
(c) Novel LDR and depth synthesis
Figure 1: High Dynamic Range Neural Scene Flow Fields (HDR-NSFF) reconstruct dynamic HDR radiance field from (a) alternating-ext 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.
ABSTRACT
Radiance real-world scenes typically spans much wider dynamic range than what standard cameras can capture. While conventional HDR methods merge alternating-epposurr frames, these approaches are inherently constrained 2D pixel-level alignment, often leading to ghosting artifacts and temporal inconsis- tency in dynamic scenes. address these limitations, we present HDR-NSFF, paradigm shift from 2D-based merging to 4D spatio-temporal modeling. Our framework reconstructs dynamic HDR radiance fields from alternating- expoosure monocular videos by representing the scene 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 robust- ness by (i) extending semantic-based optical flow with DINO features to achieve exposure-invane invariant motion estimation, and (ii) incorporating generative prior as a regularizer to compensate for limited observation in monocular captures and saturation-induced information loss. To evaluate HDR space-time view synthe- sis, we present the first real-world 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-ar performance novel space-time view synthesis. Project page: https://shin-done sowelii-dddnn-yyo...oo yew.. yow...ihhh......HDD........
continuous function of space
dataset specifically designed for

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Published as a conference paper at ICLR 2026
Input
Output
AAN-IDR
HORFlOw
SECTIDE
Allemasing--xppen expossur vidco
1+
++20
Comparison of HDR video reconstruction on training views. Given alternating- exposuree 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 2:
INTRODUCTION
Radiance of real-world scenes typically spans much wider dynamic range than what standard digital sensors can capture. As result, captures with standard cameras often suffer from overexposed highlights or underexposed shadows, leading to severe information loss in critical regions during the recording process. While high dynamic range (HDR) imaging aims to recover this lost information by merging anternatinn-expposee
frames, conventional methods are fundamentally constrained to the 2D image plane, Existing video-based approaches (Kalantari et al. 2017 Chung and Cho 2023 Xu et al.) 2024: Cui et al.) 2024) typically rely on 2D pixel-level alignment within narrow temporal window of only 3 to frames. Since these methods lack physical understanding of the 3D scene, they often suffer from significant color drift and geometric flickering, distant frames not constrained to maintain radiometric and spatio-temporal consistency (see Fig. 2
To address these limitations, we propose paradigm shift from 2D pixel-based fusion 4D spatio- temporal modeling. In this work, we introduce HDR-NSFF, a novel framework that jointly re- constructs HDR radiance, 3D scene flow, geometry, and tone-mapping from eeeeruu monocular videos. Our method maps the entire video sequence into a single, unified 4D scene representation. This approach establishes global temporal receptive field, ensuring that both ap- pearance and geometry remain consistent across the entire duration of the video, regardless of the temporal distance between frames. By representing the scene as continuous function of space and time, HDR-NSFF simplifies complex dynamics into physically plausible 3D scene flow. Our approach built upon 4D neural radiance field formulation and compatible with emerging dynamic representations such as 4D Gaussian Splatting, making it broadly applicable across modern 4D scene reconstruction pipelines.
The reconstruction of dynamic HDR fields from monocular input is a highly ill-posed problem due to the coupled effects of exposure variance and limited-view observations. To handle these challenges, build upon the 4D representation of Neural Scene Flow Fields (NSFF) (Li et al. 2021) and jointly optimize with a learnable tone-mat mapping (TM) module. This allows the model to bridge the gap between varying LDR observations and the underlying HDR radiance in end-to-end manner.
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A critical challenge is that severe color inconsistency across frames degrades the reliability of standard motion priors (Teed and Deng 2020). However, while pixel-level appearances fluctuate with exposure changes, the underlying semantic characteristics of an object remain invariant. Consequently, utilize semantic features from DINOv2 (Oquab et al. 2023) to predict exposure-invariant dense flow, providing stable and consistent motion cues under extreme exposure variations.
Another major challenge is the inherent information scarcity of alternating-exponn exposure monocular videos, where observations are limited to single viewpoint and frequently compromised by saturation. To address this, we incorporate a generative prior (Wu et al. 2025) as a regularizer to distill missing information into the radiance field. Instead of relying solely on degraded physical observations, our framework bootstraps the reconstruction by synthesizing enhanced multi- view supervision for unseen perspectives. This process allows HDR-NSFF to recover semantically plausible structures in regions where original pixels are completely lost, effectively mitigating the limited-view and saturation constraints of monocular capture.
To evaluate the proposed method, we construct the first real-world HDR-GoPro dataset. Captured with nine synchronized cameras, this dataset provides the first benchmark for dynamic HDR reconstruction with explicit multi-exposure variations across viewpoints. Extensive experiments on both real-world and synthetic data demonstrate that HDR-NSFF achieves state-of-the the-trr performance in novel view and time synthesis, recovering fine radiance details where previous methods MkrrtnnBrualle et al.) 2021; Wu et al. 2024b Zhu et af.] 2024 Wu et al., 2024a) fail to produce consistent results. We further empirically validate the generality of our framework through additional experiments a 4D) Gaussian Splatting pipeline and 2D-to-4D HDR reconstruction baselines, confirming that our design representation-agnosii (see Appendix for details). To summarize, our key contributions are:
4D HDR Framework: We propose HDR-NSFF, shifting from 2D pixel-fusion to 4D spatio- temporal modeling to ensure global coherence and physical consistency across entire video.
We introduce exposure-robus motion estimation using semantic invariance of DINOv2 and employ generative priors as a regularizer to compensate for information loss in saturated, monocular views.
Robust Learning Strategies:
We provide the first real-world benchmark for cross-exposiee space-tima view synthesis, featuring nine synchronized cameras with explicit multi-exposure variations.
HDR-GoPro Dataset:
RELATED WORK
High Dynamic Range Video Reconstruction. High dynamic range (HDR) imaging from multi- exposure inputs has been extensively studied in computational photography. Early image- approaches combine differently exposed photographs by estimating radiometric response functions and fusing measurements into HDR radiance maps (Debevec et al. [023 MMissunaaa and Nayar 1999).
More recent works model HDR recovery using low-rank constraints across exposure stacks, enabling robustness to misalignment and outliers (Oh et al. 2013 2014). Building upon these foundations, subsequent works extend HDR reconstruction to video by aligning and fusing alternating-exposure
LDR frames (Kang et al., 2003 Kalantari et al. 2013 2017 Chen et al. 2021 Chung and Cho 2023
Xu al. 2024; Cui et al.) 2024). These approaches typically rely on optical flow or CNN-based alignment in 2D, followed by refinement to suppress ghosting. While effective for moderate motion, they remain vulnerable to occlusions, large displacements, and exposure-induced inconsistencies. In contrast, our work reconstructs HDR video in 4D) enabling consistent rendering.
Dynamic Scene Reconstruction. NcRF-based methods such as NSFF Li et al. 2021). DynIBaR (Li et al. (023). HyperNeRF (Park et al. 202T), and factorized grid models like HexPlane (Cao and Johnson| 2023) and K-Planes (Fridovich-Keil et al. 2023) have advanced free-viewpoint rendering of dynamic scenes. These methods represent scene as a continuous function of space and time, sometimes augmented with deformation fields or canonical templates. They can synthesize novel views even novel time steps. In parallel, 3D Gaussian Splatting has recently been extended to dynamic settings through 4DGS (Wu et al., 2024b), MotionGS (Zhu et al.] 2024), Gaussian Marbles (Stearns et al. 2024), DeformableGS (Ynng et al. 2024), and MoSca (Lei et al., 2025) achieving high efficiency and real-time rendering. Despite their success, all of these methods assume photometrically consistent LDR inputs and do not address the challenges of HDR content. Thus,
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HDR Field
Tone mapping
White Balance
CRF
Frame
Frame
Frame 1+1
Rendered HDR
Rendered LDR
Inputs
Readering
Loss
Semaniic
Flow
Monodepihh
GT LDR
3D Scene Flow
Monodepphh
Rendered LDR
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 the target frame. which is tone- mapped to LDR via a white-balance and camera-responss function module. Photometric loss with the ground-truth LDR images, along with optical flow and depth constraints from off aff-the-shelf models, jointly optimize both the scene flow fields and tone-mapping module in end-to-end manner.
they struggle to faithfully represent scenes with extreme lighting variations, whereas our approach explicitly targets HDR reconstruction of dynamic radiance fields.
High Dynamic Range Novel View Synthesis. Several recent works integrate HDR modeling into
volumetric representations, mainly for static scenes. HDR-NeRF (Huang al.) 2022) and HDR- Plenoxel (Jun-Seong et al. (2022) model radiance together with tone-mapping exposure functions, enabling HDR novel view synthesis from multi-exposure data. GaussHDR (Liu et al., 2025) extends HDR reconstruction to Gaussian Splatting with local tone mapping, while LTM-NeRI (Huang et al.) 2024) embeds spatially varying tone mapping directly into NeRF. These works demonstrate the benefits of HDR-aware radiance fields but assume static content. The most relevant to work is HDR-HexPllne (Wu et al. 2024a)), which extends a factorized grid representation to dynamic HDR scenes by learning per-image exposure mappings. However, it does not explicitly model 3D motion, limiting its ability to represent complex dynamics and to perform temporal synthesis. In contrast, our method incorporates explicit motion modeling. allowing robust HDR reconstruction from real-world alternating-exposuree videos and supporting both novel-view and novel-time rendering.
3 PRELIMINARY
Neural Scene Flow Fields. Neural Scene Flow Fields (NSFF) (Li et al., 202I) extend NeRF (Mildenhall et al. 2021) dynamic environments by representing scene spatio-temporal radiance field. To handle moving objects, NSFF decomposes the scene into a static branch F and a dynamic branch Jully where the latter is conditioned time The core innovation of NSFF is the explicit modeling of 3D scene flow F, which predicts the displacement of 3D points between adjacent frames:
(ct o Fe = (x, d,t).
This scene flow allows the model to warp 3D points across time, facilitating temporal consistency construints 3D space rather than 2D pixels. The static branch models time-invariant geometry, and both branches are fused via blending weight
Ö(r,t) [f" T(z, (o()))((()))))
(l)) (z) dz.
By optimizing these components jointly, NSFF reconstructs continuous 4D representation that maintains geometric coherence over time, providing robust foundation for our HDR reconstruction in dynamic settings. Details are provided in the Appendix
4

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Published as a conference paper at ICLR 2026
Inputs
RAFT (Gamma)
RAFT (Finetuned)
Ours
Forward
Image
Backward
Image
Forward
Imuge
Image 2
ward
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 produce accurate results. While fine-tuning on synthetic data RAFT (Finetuned) shows moderate improvement, our proposed semantic-based approach achieves superior accuracy.
METHOD
HDR-NSFF reconstructs dynamic HDR radiance fields by jointly optimizing HDR radiance, 3D scene flow, geometry, and tone-mapping from alternating-exposure monocular videos. Building upon the 4D representation of NSFF (Sec. 3 our framework introduces a unified pipeline that disentangles appearance and motion under varying exposures. As illustrated in Fig. 3 HDR-NSFF integrates three core components to achieve physically plausible and temporally coherent HDR reconstruc- tion: (i) joint optimization strategy incorporating learnable cone-mapinng module, (Sec. (.I). (ii) exposure-robust semantic flow estimation for reliable motion learning (Sec. (.2), (iii) generative prior regularization to compensate for the fimited-view and saturation constraints input video (Sec.
4.1 TONE-MAPPING
A major challenge in HDR reconstruction is the need to model the nonlinear relationship between LDR observations and the underlying HDR radiance. explicitly account for this nonlinearity, we introduce a learnable tone-mapping module T with radiometric parameter that maps rendered HDR radiance to the LDR domain:
C= = g
where applies per-channel white balance correction and denotes the camera response function (CRF). To ensure stable optimization under extreme exposures, we employ a ceaky-thresholde CRF that mitigates saturation effects, along with smoothness regularization that encourages physically plausible CRF shapes through second-order derivative penalties. These components provide both flexibility and regularization, enabling T to form consistent HDR supervision across varying exposure levels and maintain a coherent radiance field in 4D space. Details are provided the Appendix
4.2 SEMANTIC BASED OPTICAL FLOW
A key challenge in reconstructing HDR dynamic scenes from alternating ercemnatin--eppsuuree video is that severe trame-toframee color inconsistencies degrade the reliability of conventional optical flow methods (Ted and Deng 2020) (see Fig. We overcome this leveraging the insight that while

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Dynamic HDR Radiance Fields
Novel views at any time
Trainnig view N: view
Generative Prior
Progressive update
Enhanced novel views
Figure 5: Generative prior (GP) pipeline for HDR dynamic radiance fields optimization. Unseen novel views are first rendered and then refined via GP restore details in regions with broken cor- respondences. These enhanced views serve as pseudo-labels for the progressive optimization HDR dynamic radiance fields. This bootstrapping mechanism stabilizes correspondences, mitigating issues related to sparse viewpoints and saturated pixels monocular videos with alternating exposures.
pixel-level appearances fluctuate with exposure changes, the semantic characteristies remain invariant. Consequently, transition from color-dependent matching to semantic-based motion estimation.
In this context, we utilize the robust embedding space DINOv2 (Oquab et al. 2023). Beyond its known resilience to photometric corruptions (Hendrycks and Dietterich 2019 we empirically observe that DINOv2 features maintain consistency in varying exposure settings (see Appendix (S2). Based these observations, adopt estimation backbone with task-specific modifications.
(Tumanyan et al. 2024)
motion
To prevent the accumulation of tracking errors long sequences, re-initialize tracking points each timestep. This allows us to estimate precise bidirectional adjacent-frame flow as required by our pipeline. Furthermore, DINO-Tracker to dynamic regions, effectively filtering out tracking noise in the background. As result, our semantic flow provides the stable and consistent motion cues (see Fig.
incorporate motion masks from SAM2 (Ravi al. 2024) confine
4.3 GENERATIVE PRIOR AS REGULARIZER
Reconstructing dynamic HDR scenes from monocular videos is particularly challenging due the inherent information scarcity. At any given timestamp, the scene is observed from only single viewpoint, which is frequently compromised by saturation. Since these degraded physical observations cannot directly provide sufficient geometric and radiometric cues, we incorporate generative prior (Wu al. (025) regularizer distill missing information into the radiance field.
The core strategy is to bootstrap the 4D reconstruction by transforming the ill-posed monocular task into a oseudo-multi-viee supervision for unseen perspectives, enabling HDR-NSFF to recover semantically plausible structures in regions where original pixels are lost. During optimization, we periodically render a candidate novel viewpoint Ć. We then obtain a generative enhancement Crea G(C, CTee by utilizing the generative prior G. conditioned on the available training view Ce to maintain spatial consistency. (rgen serve as pseudo observations, and we enforce alignment via patch-wiss perceptual loss:
optimization problem. Specifically, our framework synthesizes enhanced
Egen Σ @((C)) PEP
((C
where & denotes the perceptual encoder and indexes spatial patches. This regularization encourages the model to fill in saturated unobserved regions with semantically consistent content. To prevent the prior from introducing hallucinations or overriding physical truths, we carefully control its influence through scheduled activation. The generative loss is applied only after initial warm-up

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Published as a conference paper at ICLR 2026
Tumbler
Bear
Big jump
Pointing walk
low
mid
high
low
high mid low high
(a) Capture setup
(b) Sampled sequences
Figure Evaluation setup and sampled sequences from our proposed GoPro dataset. To evaluate novel view synthesis, use nine GoPro Hero 13 Black cameras synchronized record multi-view video at three exposures (mid, low, high). We construct alternating eexppair monocular video by selecting one frame per time step across exposures, and use the remaining views for evaluation.
period Twarn to ensure the underlying geometry is reasonably established:
0, Pgens
1 < Tarm
Ogen (t)
(5)
t 2 Twam
where T warm and sampling probability Pgen are 200K and 0.1, respectively. The generative loss is:
Lgen O'gen (t) Bgen Egen;
with 8gen controlling the overall contribution as regularizer. Figure 5 shows that this bootstrapping process effectively mitigates the limited-view and saturation constraints, ensuring geometric fidelity and radiance completeness across space and time, More details are provided in the Appendix C
4.4 OBJECTIVE FUNCTION
We train both the neural scene flow fields and the tone-mapping module minimizing the Mean Absolute Error (MAE) between rendered LDR views and ground-truth frames. Following NSFF (Li et al. 2021). we replace the rendered color Ó' with our cone-mapped output T(E), where É denotes the rendered HDR radiance. The superscript eb denotes the combined rendering that fuses static and dynamic components of the scene. The photometric losses are:
Leh
IT (r:)) C,(rs)l,,, and Σ
Cpooto
Σ
IIT( -G())l JEN(()
Σ
where denotes camera ray. Here, Ég--(ri) denotes the HDR radiance warped from frame j to i. We also adopt the optical flow and single-view depth prior, denoted LFlow and Ldepth to regularize monocular reconstruction followed by NSFF (Li et al. 2021). For the CRF and generative prior objective functions, we apply Lamooth and Lgen, respectively. The total objective function of our HDR-NSFF is as follows:
L Leb + Lphono + Pams Cdata Bregfreg + Samooth + Lgen: where 3 are coefficients weight each term. The details can be found in Appendix
(9)
4.5 DATASETS
Proposed HDR-GoPro Dataset. To evaluate HDR space-time view synthesis, we construct the first real-world dataset using nine synchronized GoPro cameras arranged in a nearly parallel configuration. The cameras are divided into three groups, each assigned to a different exposure level (low, mid, frames,
high). This setup allows to evaluate whether the model, trained on can successfully reconstruct the underlying radiance and synthesize views at any target exposure. The dataset comprises 12 diverse indoor and outdoor scenes featuring complex motions (see Fig. 6)
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GT
NT
HDR-Hexplane
Ous
LDX
view
HUR-Hoxplase
HDD
BROOA AB
MARMING
GI
N&RE-WT
HDR Hesplane
LD
Ours renderdd view
HDR-Hecpleee
Oura
Figure 7: Qualitative results of novel view synthesis on GoPro dataset. The odd nummeed rows show the LDR-renderee 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 cone-mapped HDR novel views. Compared with HDR-HHexPaan (Wul et al. 2024a), our approach produces more accurate radiance, geometry, and motion representations.
EXPERIMENTS
In this section, we evaluate the effectiveness of HDR-NSFF in reconstructing high-quality 4D HDR scenes from ceemmating-xpposun monocular videos. We first present the primary results on novel view and time synthesis using both synthetic and our newly constructed real-world HDR- Gooro datasets (Sec. 5.T). To validate the fidelity of the reconstructed radiance, we also provide qualitative comparison with ground-truth HDR images and histogram analyses. Then, we conduct an in-depth analysis of our robust learning strategies (Sec. 5.2). where we justify our design choices through controlled experiments under extreme exposure variations. For all HDR visualizations, we employ consistent Photomatix Pro tone-mappan onnemmapinng operator to ensure fair qualitative comparison.
5.1 RESULTS
Novel view synthesis. We evaluate novel view synthesis on our proposed HDR-GoPro dataset. At each timestamp, we use single camera's 's frame for training and reserve the remaining eight viewpoints, which capture the same moment at different exposures as ground truth. During testing. the reconstructed HDR radiance is tone-mane mapped to the target exposure for quantitative comparison (e.g., PSNR, SSIM, LPIPS) against these LDR references. This ensures a rigorous assessment of both temporal coherence and exposure-invariat geometric reconstruction. Table shows that our approach
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Foll
Dynamic only
Full
Dynamic
Methods
PSNR1
561M1
LP1ES
PSNK"
SSEM
LPIPS]
Methods
PSNK
SSIM
LPIPS]
PSNKT
LPIPS]
NSFF
0.3061
17.59
0.5473
0,230
NSFF
15,95
0,6457
0.1388
16.04
0.565]
0.1527
4DGS
20.94
0,3976
0.154
17.83
12:35
0.5534
0.2330
0.4696
HDR-HexPPeee
NeRF-WT
29.05
$1.10
0.5366
0.9055
0.0527
0,0342
23887
21,50
0.7490
0.7999
0.1071
0,0895
NT
29.70
0.333
0.598
19:25
06335
0.1770
0.0576
HDR HesPlane
20.70
0.6694
0.1917
20.55
0,6299
0.1716
Ours twdo GP DT)
31.04
0.9364
0,0521
26.93
0,8068
0,1048
Table 3: Averaged quantitative results of novel
CIP)
32.66
0.157
25.65
0.1012
32.65
25.50
0,0972
view and time synthesis on synthetic data. Our method outperforms baseline models.
Table 1: Averaged quantitative results of novel
view synthesis GoPro dataset. Ours achieves the best overall performance, with DINO-Tracker (DT) offering the strongest improvement in motion- consistent reconstruction and the generative prior (GP) further enhancing perceptual quality.
Full
Dynami: only
Mothads
PSNR1
SSIMI
LPIPS
PSNR1
SSIM
1PPPS]]
NSFF
19.0
0.1976
1834
0.5873
0.2531
HDR- HexPlaee
20.46
0.6583
0.1933
19.59
0.1655
Ours tw/o GP DT)
31.31
0.9392
0.0488
23.85
0.7979
0.1372
Ours twi CIP!
32.79
0.9151
00996
25:00
0.8055
0.1378
Figure 8: Qualitative results of novel view and time synthesis on synthetic data. Since our ap- proach explicitly models scene flow, it excels at time interpolation.
Ours
32.75
0.9488
0.994
25.26
0,8070
0.1359
Table 2: Averaged quantitative results of novel
view and time synthesis on GoPro dataset. Our method outperforms baseline models.
achieves significant improvements in rendering fidelity compared to baselines, both in highly dynamic regions and across the entire scene. Figure 7 shows its effectiveness in reconstructing HDR scenes with fine detail across varying exposures. Methods without appearance embedding (NSFF (Li et al., 202T), 4DGS (Wu et al.) 2024b), MotionGS (Zhu et al. 2024)) fail to reconstruct consistent HDR views under alternating exposures. NeRF-WT (Quei-An 2020) and HDR-Hexplane (Wu et al. 2024a)
provide limited robustness, but still struggle in real-world settings.
Novel view and time synthesis. We further evaluate novel view and time synthesis to assess the model's ability to interpolate dynamic motion from temporally sparse observations (see Fig. []. We train the model using only the even-indexed frames of the input video, effectively doubling the temporal gap between training samples. For evaluation, we then synthesize the held-out odd-indexed frames from unseen camera viewpoints. This task requires the model to simultancously interpolate 3D scene flow for temporal consistency and synthesize novel spatial views. As shown in Table 3 our method consistently outperforms competing models across all metrics, demonstrating superior robustness in handling complex dynamics even with reduced temporal sampling.
We also conduct this joint evaluation on our real-world HDR-GoPro dataset by synthesizing scenes at held-out time instances and camera viewpoints. As shown in Table 2 our model consistently surpasses all baseline methods, confirming its ability to handle complex real-world dynamics with high radiometric and geometric accuracy. The success of HDR- stems from its explicit modeling of 3D scene flow, which enables reliable synthesis across both space and time. In contrast, grid- based representations like HDR-HexPlane (Wu et al., 2024a) lack explicit motion modeling, which inherently limits their capacity for accurate space-time time interpolation in dynamic HDR scenes.
Qualitative comparison of HDR reconstruction. To validate our HDR reconstruction, qualita- tively compare our results with ground-truth HDR images (see Fig. 9. Tone-mapped HDR views from our model closely match ground truth, preserving fine details in both under- and overexposed regions. Histograms of pixel intensities further show that our reconstructions cover the full radiance range, recovering values from very low high intensities. In addition, novel LDR views rendered at multiple exposures confirm that our method accurately controls exposure.
5.2 EXPOSURE ROBUST LEARNING STRATEGIES
Depth analysis under varying exposure. We assess the robustness of off-the-shelf depth estimators under exposure variation by synthetically generating +2 EV versions of input im- ages. RGB images are first converted into pseudo-RAW using learned ISP inversion

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Choos
Low exp. LDR
Mid exp. 1DR
High esp. LDR
Tume-mapped IIDR
GT Tone-mapped IIDR
Demsily of HIDR view
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-trutt HDR references. We also visualize histograms of our HDR images and ground truth. For better visualization, we plot HDR histogram using smoothed KDE method.
model (Xing et al., 2021), after which +2 EV renderings produced via standard sRGB map- ping. Following the protocol of Ke et al. (Ke et al. 2024), we evaluate AbsRel on NYUv2
and ScanNet (Silberman et al.) 2012 Dai
NYUv2
SeanNet
et al. (017). As shown in Table 4 while all methods degrade under #2 EV shifts, Depth-Anything- V2 remains significantly more robust than alternatives. Based on this observation. we adopt Depth-Anything V2 as the geometric prior our pipeline.
Methods
Original
+ZEV
-2EV
Original
+2EV
-2EV
MiDaS
9.08
13.68
9.35
8.66
13.78
10.22
12.96
8.27
9.57
Marigold
5.81
11:26
5.06
7.24
14,26
8.33
Depth-Anything-22
4.87
7.63
5.10
4.2
10.57
6.36
Table Depth estimation results under exposure variance. We employ AbsRel as the evaluation metric.
Tone-mapping module analysis. Prior HDR radiance studies explored three CRF designs: a non-learnable fixed CRF (Wu et al., 2024a). fully
Full
Dynamic only
Methods
PSNRT
SSIMI
LPIPS
PSNRT
SSIMT
LPIPS
w/o Tone mupping
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
learnable MLP-based CRF (Huang (ct al.) 2022), and a piecewise parametric CRF with per-channel white-balance
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.
balance factors (Jun-Seong et al. 2022). The fixed CRF offers strong regularization but insufficient flexibility, whereas the MLP CRF is overly flexible and often unstable. We adopt the piecewise CRF, which provides a balanced formulation. On our HDR-GoPro dataset, piecewise CRF achieves the best novel-view synthesis performance (Table 5), indicating that moderate flexibility with structured regularization is most effective under varying exposures.
Beyond our main experiments, validate the generality of our framework by integrating our modules into a 4D Gaussian Splatting (4DGS) pipeline (Lei et al. 2025) and comparing against 2D-to-4D HDR reconstruction baselines. Detailed results are provided in the Appendix
CONCLUSION
In this work, we introduced HDR-NSFF, the first framework to jointly reconstruct HDR radiance, 3D scene flow, geometry, and tone-mapping from alternating-eppssuee monocular videos. By shifting the paradigm from 2D pixel-based fusion 4D spatio-temporal modeling, our approach overcomes the fundamental limitations of prior methods, such as color drift and geometric flickering. We leveraged the semantic invariance of DINOv2 for robust motion estimation and incorporated 3D- aware generative priors regularizer to recover information in saturated regions. Extensive experiments on synthetic and our newly proposed HDR-GoPro dataset demonstrate that HDR-NSFF consistently outperforms state-of-the-art baselines in novel view and time synthesis.
Limitations. Our method relies on pre-computed camera pose by COLMAP (Schonberger and Frahm 2016), which can be difficult to estimate in extreme exposure-varying scenes. Furthermore, our current model does not explicitly handle motion blur from long exposures. Addressing these efficiency and robustness challenges remains important direction for future 4D HDR research.
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ACANOWLEDGMENNT
We thank Lee Jung-Mok, GeonU Kim, and Donghyun Kim for their assistance in data acquisition, and the members of AMILab for helpful discussions.
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2 ASS-2025-254433 Physically grounded Intelligence: A Dual Competency Approach to Embodied AGI through Constructing and Reasoning in the Real World), Institute of Information & communications Technology Planning & National AI Research Lab Project), the National Research Foundation Korea (NRF) grant funded by the Korea government (MSIT) (No. RS RS-2024-00358 135, Corner Vision: Learning to Look Around the Corner through Multi-modae Signals), and the InnoCORE, program of the Ministry of Science and ICT (25-InoCCORF-01).
Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2024-0045783
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APPENDIX
This appendix provides additional technical details and experimental results complementary to the main manuscript. Section A describes the implementation details, including hardware configuration and training setups. Section elaborates on the Neural Scene Flow Fields (NSFF) framework and its regularization terms, while Section C details our Hpe-specific components, including conemmapingg semantic tracking, and the generative prior formulation.
Additional experimental results and ablation studies are presented in Section We further demon- strate the general utility of our modules through an extension to 3DGS-based reconstruction in Section E followed by comparative analysis against 2D-to-4D HDR reconstruction pipelines dataset.
in Section Finally, Section G provides extensive qualitative results on the We highly recommend watching the supplementary video for a better assessment the temporal coherence and dynamic HDR rendering quality of our results.
CONTENTS
Sec.
Implementation Details.
Sec.
Details of Neural Scene Flow Fields.
Sec.
Details High Dynamic Range Neural Scene Flow Fields.
Sec.
Additional Experiment Results.
Sec.
Extension to EDGS-based Dynamic Scene Reconstruction.
Sec.
2D-to-D HDR Reconstruction.
Sec.
Qualitative results HDR-GoPro dataset.
A IMPLEMENTATION DETAILS
A.1 COUNTERPARTS
In this chapter, we briefly explain the method we compared as a counterpart in our experiments.
NeRF-WT. NeRF-W (Martin- MMartin-rrualla et al. 2021 introduces per-image appearance and transient embedding, modeling to handle dynamic changes such as lighting variations and moving objects. In experiments, adapted NeRF-W appearance embedding for ISP modeling and transient part for scene dynamics. We follow the hyperparameters given in the codebase. For implementation we used the codebase in https
dynamic HDR video (named NeRF-WT) using
//github. com/ www.k23/nererrree
HDR-Hexplane. HDR-Hexplane (Wu al. 2024a) adopted Hexplane (Cao and Johnson 2023
for the dynamic 3D representation and MLP with exposure embeddings accompanied with fixed gamma function optimize ISP module. We follow the hyperparameters following manuscript. For implementation we used the codebase in https::/
HexP lane
A.2 DATASET
Synthetic. We select four synthetic scenes for evaluation: Lego, Mutant, Jumping Jack, and Stand Up. Each image has resolution of 800 800, with exposure values spanning from -2EV to SEV. To maximize the influence of exposure change, we carefully adjust the camera viewpoints and lighting directions.
The sampling rate is determined based on the motion speed of each scene. Specifically, the Lego scene is subsampled by selecting every 10th frame, whereas the remaining scenes sampled by skipping every two frames.
Real. For the real dataset, preset exposure time for each camera before acquisition. We set expo- sure time differently for each sequence. Sequence lengths and corresponding exposure information detailed in the Table ST All sequences are synchronized using the GoPro software.
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Name
Exp.
Time
# of frames
jump
324
960
2800
7680
Side walk
324
960
2850
7680
Jumping jack
324
720
1920
7680
Pointing walk
324
720
1920
7680
Tube toss
324
720 1922
7680
Bear
324
120 450 1933
Dog
324
170: 140 1920
Tumbler
324
120
160 1920
Fire extinguisher
324
480
860
1920
Laptop
324
480
960
1420
Bag
324
170
440 10220
Ball
324
120 180 1920
Table S1: Parameter settings for real dataset.
A.3 EXPERIMENTAL SETUP
To facilitate understanding of the experimental setup employed for the real dataset experiments, we provide an illustrative diagram in Fig. [S the novel view synthesis experiment, performance is evaluated by measuring the differences between synthesized results and the images captured from cameras that excluded from the training for all camera views i. In the novel view and time synthesis experiment, we evaluate performance by holding out certain segments the time sequence and measuring how accurately these withheld segments inferred.
High
Mid
Low
High
Mid
i
Low
Mid
High
Low
i
i
i+ 2
i
+ 4
High
Mid
Low
High
Mid
(b)
+ 8
+ 7
? 6
++
Low
Mid
High
Low
Train
Test
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.
B DETAILS OF NEURAL SCENE FLOW FIELDS
To model dynamic scenes, NSFF (ii et al. 2021) extend the concept of NeRF (Mildenhall et al. 202T) by representing 3D motion as scene flow fields. NSFF learns a combination of static and dynamic NeRF representations. The dynamic model, denoted as Fely explicitly models view and time dependent variations by incorporating time i additional input. Beyond color and density, also predicts forward and backward 3D scene flow F;=(ff- fe-t-1) and occlusion weights
W W.=[w;+thh 1))t-1 handle 3D motion disocclusion:
(ce, Tt Fr, Wt) = Fi (x, d)
(10)
To supervise scene flow estimation, NSFF uses temporal photometric consistency. Specifically, for each time scene flow is predicted for the 3D points sampled along rays, and this predicted flow is
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used to warp corresponding points from neighboring times j E M(t)) time i. The color and opacity information of the warped points is then used to render the image at time i:
()-((r=)= I. 1)())
(ro-a))), dz,
(11)
where T=-s(=) r=(=))))))))))))))))()))))))-r + +f)-)(((())))
(12)
Temporal photometric consistency is enforced by minimizing the mean squared error (MSE) between the warped rendered view and the ground-truth image:
Cphoto Σ
Σ JEN(()
-
(13)
The static NeRF, F7 represents a time-invariant scene using a multilayer perceptron (MLP). Given an imput position x and view direction d. outputs the RGB color c. volume density o, and an unsupervised 3D mixing weight that determines the blending between static and dynamic components:
(c, = F (x,
(14)
Here, & and og denote the color and volume density at position x at time I. The final color computed by blending the static and dynamic components using the following rendering equation:
(ri) = [ TT of (2) dz,
(15)
where (x)c((((() a linear combination of static and dynamic scene components, weighted by v()):
(2) (2) =(=)c(=))(z)) (1 (z))))
(16)
T; represents the transmittance at time while 2n and 2y denote the near and far depths along the ray. The final rendered output (ra((r) photometric loss:
(r:) is optimized against the ground-truth pixel color (r(s:) using
Leb = E10e - C.(r.)
(17)
Reconstructing dynamic scenes from monocular input is inherently ill-posed, and relying solely on photometric consistency often leads to convergence at poor local minima. Therefore, NSFF incorporates three additional guided losses: a term enforcing monocular depth and optical flow consistency, a motion trajectory term promoting cycle-consistency and spatiotemporal smoothness, and compactness prior encouraging binary scene decomposition and reducing floaters via entropy and distortion losses.
Following section, we elaborate on data-driven prior loss (Flow loss and Single-view depth loss) and additional regularization terms introduced by NSFF (Li et al. 2021): Scene Flow Cycle Consistency and Low-Level regularization term. We employ additional regularization terms consistently in both our model and NSFF.
Flow Loss. Flow loss operates by minimizing the discrepancy between observed 2D pixel correspon- dences, computed from pretrained optical flow networks and predicted 2D pixel correspondences, obtained by projecting predicted 3D scene flows. This aligns 3D scene flow with pretrained 2D motion estimation.
Given two adjacent frames at times i and j = i 1, Lflow is calculated follows. Let p. represent a pixel location at frame i. The corresponding pixel location at frame j. denoted by Pi-jj, can be computed using pretrained 2D motion estimation Ui-+j as Pi-+ p: + Ug-+j-
The model predicts an expected scene flow (ra) corresponding to 3D location (r,r,) along the ray passing through the pixel Pa via volumetric rendering. Thus, the predicted 3D displacement can be expressed as (r.) + (ra)) Then, by applying the perspective projection operator IJ, corresponding to the camera viewpoint at frame j the expected 2D pixel position (r-)((r)) at frame j is calculated as:
(r:)(r:) = 11, (X.(rr) + Pres((r)).
(X.(r))+f.>>r>:))))
(18)
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Finally, the geometric consisteney loss is computed by measuring the discrepancy between these two pixel positions (observed vs. predicted) using the L1-norm:
Laww Σ Σ
(19)
(E(+1)
Single-view Depth Prior. Encourages rendered depths to match predictions from a pretrained depth model:
Laepth
Σ (r.) (ra)ll,,
(20)
Ti
where the superscript (+) denotes scale-shift invariant normalization, These priors combined into:
Ldam = Lnow + Pteeth Leepth-
(21)
Scene Flow Cycle Consistency. To ensure plausible scene motion, the loss ensures coherence
between forward and backward predicted scene flows for adjacent frames, mathematically defined as:
Laye
Σ Σ ((nlllfi ()=
(22)
where (1;) indicates the predicted displacement (scene flow) of point 2; from time to j.
Low-Level Regularization. Spatial-temporra smoothness is enforced through (1 regularization scene flow estimated between neighboring sampled 3D points along rays. This encourages 3D point trajectories be piecewise linear. Another sparsity regularization term, calculating estimation is also applied. This encourage minimal scene flow magnitudes across most spatial regions. It is composed of three equally weighted components: spatial smoothness, temporal smoothness, and minimal flow magnitude:
71 loss in flow
Lyeg = Cap + Ciemp + Cmin.
Spatial Smoothness. Following NSFF, the spatial smoothness term encourages nearby 3D samples along the same camera ray to predict similar scene flows. For each sampled 3D location x, on ray r,, we consider its neighboring samples (xx;) and penalize the weighted &, discrepancy:
Cap Σ Σ Σ
w dist (x., (51,s(x)) /x,,((,)))))
(1)
(EE
where the weight is based on Euclidean distance: w wiist (x, exp(-2l|xx yl12).
Temporal Smoothness. The temporal term encourages each 3D trajectory to maintain low kinetic energy. This is implemented by minimizing the squared norm of the sum of forward and backward
scene flows:
1 1/2 Σ llfi (x;)- 2
Cremp
Minimal Flow Prior. Finally, following the observation that most points in the scene exhibit small motion, we impose an l, penalty on all predicted scene flows to encourage near-zero flows where appropriate:
Cmin
(x)
SE(==I)
C DETAILS OF HIGH DYNAMIC RANGE NEURAL SCENE FLOW FIELDS
Our method is built upon the NSFF framework and therefore inherits its core loss formulation and optimization structure. However, reconstructing HDR dynamic radiance fields from alternating- exposure monocular videos introduces unique challenges not addressed in the original NSFF design. To handle severe exposure fluctuations, saturation artifacts, and inconsistent appearance across viewpoints, we adapt NSFF's formulation by modifying the photometric loss (Eq. 8 and combined loss (Eq. 7. and by introducing additional regularizers tailored for HDR reconstruction. Specifically, HDR-NSEF incorporates
(i) physically informed cone-mapping module, and (ii) generative prior that recovers saturated or missing information. We describe each of these components in detail below, along with the updated objective function used for end-to-end optimization.
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C.I TONE-MAPE MAPPING
Our goal is to reconstruct HDR dynamic radiance fields, encompassing both 3D space and motion, from 2D multi-exposure LDR RGB images. A crucial component in this process the tone- mapping module, which bridges the gap between varying 2D observations and a coherent 3D HDR representation. Specifically, cnnemapping module, T can be expressed as:
C = C-T(E, 0) = (())
(23)
where E denotes the rendered radiance, w the white balance correction, g the camera response function (CRF), and 0 the radiometric parameters.
The white balance function w applies per-channel scaling using the white balance parameter 0nr [wy, W/g w%) TR producing white balance-corrected image F The CRF is then applied to Ew. mapping to the final LDR image C. The CRF is parameterized as piecewise linear function, defined using 256 points uniformly sampled in the [0, 1] range. Values exceeding the dynamic range are thresholded accordingly. During training, we adopt leaky-thresholddee to reduce saturation loss in rendered images:
az,
Deaky (x) =
g(x); a
05 5
(24)
+a+1,
x x>1, 1,
Ve
where a is the thresholding coefficient. This approach ensures effective color correction and dynamic range handling during HDR-NSFF training.
We incorporate smoothness loss to enforce that CRF varies smoothly in physically plausible et al. (2023). We penalize the second-order derivative of the CRFs: It is defined as
manner Debevec manner[Debevec follows:
Lsmooth
cE(o,1] (c),
(25)
where (e) denotes the second order derivative of CRFs w.F.f. its input domain.
In the absence of a known camera response function (CRF), the choice of cone-mapping module T(..)) determines the flexibility with which HDR radiance can be effectively recovered from LDR inputs. Moreover, to build consistent HDR representations in 3D space, the tone-mapping module must also act as regularizer, preventing fluctuations in HDR results under multi-exposure conditions. This combination of flexibility and regularization largely influences the overall quality and stability of HDR field reconstruction.
C.2 DINO-TRACKE
DINO-Tracker is self-supervised framework designed to accurately track points over long sequences of video frames. Given an initial query point in an early frame of video, it estimates the trajectory of these points throughout subsequent frames. The method leverages pretrained deep features from the DINOv2-ViT (Oquab et al. 2023) model, which are refined by learning residual features via small, trainable CNN module. DINO feature and residual feature are aggregated find correspondence heatmap computed by cost volume. Lastly, additional CNN-refiner follows to further enhance matching.
Optimization is performed using several losses
Flow Loss (Lnow): Ensures predicted trajectories align closely with short-term optical flow corre- spondences.
DINO Best-Buddies Loss (Ldine-bo): Contrastively aligns refined features based on semantic matches from original DINO embeddings.
Refined Best-Buddies Loss (Lrtnsh): Similar DINO best-buddies loss but applied newly detected reliable matches among refined features.
Cycle-Consistenge Loss (T-ravee): Encourages consistency in predicted trajectories, penalizing trajectories that fail a cycle-consistency criterion.
Prior Preservation Loss (Lprior): Regularizes the refined features to remain close in norm and direction to original DINO features, ensuring semantic coherence is preserved.
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(a) Scene
(b) Scene 2
Figure S2: DINOv2 feature visualization under varying exposures. Despite large changes in brightness. DINOv2 embeddings remain consistent, showing robust clustering across different exposure levels.
Tumbler shake
Bear thread
Ours wil
stad
rai mid
ligh
Qurs wi GP
Augtictial
mid
View
View
(a) Multi-view augmentation using generative prior
(b) Ablation on generative prior (GP)
Figure S3: Effect of generative prior (GP). (a) GP provides GP provides additional plausible views from unseen views, (b) leading more consistent and sharper reconstructions.
In contrast to the original DINO-Tracker our proposed approach introduces novel utilization of this framework explicitly aimed at enhancing the robustness and accuracy of 2D dense correspondence estimation. Specifically, trained DINO-Tracker model itself. Leveraging the semantic matching capability inherent to DINO features, our method provides robust optical flow estimates even in challenging conditions such as alternating-expost video settings, where conventional texture-based methods typically degrade due information loss. Figure S2 shows that DINOv2 features robust exposure variance.
propose deriving dense matching from consecutive frames using the
C.3 GENERATIVE PRIOR FOR RECOVERING SATURATED INFORMATION
regularizer stabilize training under severe exposure inconsistencies. Generative prior provides a diffusion based enhance- ment prior that guides the radiance field toward semantically consistent reconstructions when input frames suffer from brightness fluctuations or missing details. Concretely, we periodically generate ggeudo-observatiin by enhancing intermediate renderings with the Difix prior and incorporate them into the optimization loop. This regularization not only improves geometric and radiometric stability also enforces stronger multi-view consistency in dynamic scenes, where exposure variations and motion often break correspondences across views. As result, HDR-NSFF achieves more coherent reconstructions that generalize better to unseen exposures and viewpoints.
In HDR-NSFF, additionally employ generative prior (Wu al. 2025)
Generative prior regularization. To mitigate the sparse-view limitation of monocular input, we adopt enhanced views generated via generative prior (Wu al. 2025). For these views, we apply patch-wist perceptual loss to encourage realistic and view-consistent appearance:
Lgen
Σ (lx(C,) ((Cge)) PEP
(26)
where o denotes a perceptual feature extractor, and p indexes sampled patches. Since generative priors may introduce hallucinations, carefully balance their contribution by (i) delaying their use until a stable stage of training (00K iterations), and (ii) training with enhanced views at a low probability (10%) per iteration.
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Big jump
Side walk
Full
Dynamic only
Full
Dynamic only
Method
PSNR
SSIM
I.PPSS
PSNR
SSIM
1.PPS
PSNR
SSIM
LPIPS
PSNR
SSIM
1.PIPS
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
114
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
HDR-Hexplane
21.51
0.6235
0,2117
18.04
0.5653
0.2074
19.12
0.4931
0.2392
17.01
0.6214
0.1664
Ours
30.03
0.9239
0.0596
21.72
0.7494
0.1058
29.91
0.9263
0.0515
20.39
0.7132
0.1041
Jumping jack
Pointing walk
Full
Dynamic only
Full
Dynamic only
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSFF
17.44
0.7543
0.1251
18.19
0.5493
0.1274
17.16
0.7534
0.1206
14.07
0.6198
0.1745
4DGS
19.58
0.7486
0.1239
19.15
0,5733
0.1516
19.17
0.7373
0.1319
13.52
0.3664
0.2084
MotionGS
13.82
0.2474
0.3217
11.51
0.1244
0.4059
13.72
0.2472
0.3278
10.46
0.089
0.4774
NT
30.93
0,9364
0.0384
19.94
0.6028
0.1384
27.36
0.9243
0.0488
15.26
0.5363
0,2018
HDR Hexplane
17.24
0.4671
0,2112
19.89
0.5946
0.1466
16.97
0.4398
0.2111
16.67
0.6537
0,1674
Ours
31.83
0.9457
0.0353
23.66
0.7871
0.0721
29.72
0.9338
0.0433
20.05
0,7036
0.1046
Tube
Bear thread
Full
Dynamic only
Full
Dynamic only
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSFF
17.75
0.7547
0.1237
16.94
0.7071
0.0913
15.45
0.566
0.4656
14.89
0.3535
0.5652
4DGS
19.53
0,7429
0.127
17.72
0.6961
0.087
18.20
0,7811
0.2736
12.21
0.2426
0.5023
MotionGS
13.73
0.2469
0.3227
9.85
0.1433
0.3943
12,90
0.4995
0.494
9.83
0.1518
0.6414
NeRF-WT
31.63
0,9474
0,0315
19.44
0,8194
0.0704
22.13
0,8607
0.1618
13.55
0.2925
0.4098
HDR-Hexplane
17.13
0.4732
0.2211
16.43
0.6485
0.1096
22.08
0,7903
0,2597
18.09
0.5367
0.3357
Ours
32.08
0,9482
0.0348
24.78
0,9105
0,0366
30.19
0.9224
0.1337
23.93
0.7568
0.2448
Tumbler
Dog
Method
Full
Dynamic only
Full
Dynamic only
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
L.PPS
NSFF
17.8
0.5643
0.2897
14.39
0.4618
0.3354
17.87
0,3403
0.2884
14.96
0.5193
0.3316
4DGS
22.05
0.8171
0.1753
17.54
0.6221
0.2124
0,9164
0.1069
19.42
0.7279
0.1661
MotionGS
15:41
0,5143
0.4434
12.16
0,3455
0.5881
15.26
0.4963
0.4309
11.73
0.3149
0,5667
NcRF-WT
31.97
0.9456
0.0625
20.48
0.7477
0.1733
31.7
0.9466
0.0719
20.15
0.7525
0.143
HeR-Hexplane
24.98
0.825
0.1828
22.24
0.7556
0.1909
25.02
0,8445
0.1905
20.46
0.7165
0,2318
Ours
34.92
0.9428
0.0727
27.85
0,8863
0:0927
33.14
0.944
0.0841
24.85
0.8422
0,1441
Fire extinguisher
Laptop
Full
Dynamic only
Full
Dynamic only
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSFF
21.04
0.8215
0.1300
26.5
0.6232
0.1742
23.17
0.8437
0.102
27.41
0.7219
0.1107
4DGS
22.73
0.8568
0,1324
23.94
0.6189
0.2659
23.86
0.8818
0.1023
28.34
0.8203
0.0957
MotionGS
18.34
0.6445
0.2728
20.56
0.4259
0.4292
18.64
0.6568
0,2378
20.8
0.4533
0,2908
NTRI-WT
32.78
0.9306
0.0496
24.39
0.6708
0.2079
37.4
0.9801
0.0197
29.26
0.9034
0.048
HDR Hexplane
23.57
0.8876
0.0797
28.78
0,7612
0.080
23.51
0.8993
0.0721
29.9
0.8375
0.0611
Ours
36.82
0.9686
0.0298
32.04
0.8494
0.0777
37.28
0.9759
0.0231
33.32
0.9141
0.0407
Bag
Ball
Full
Dynamic only
Full
Dynamic only
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSFF
18.33
0.6867
0,2029
19.32
0.5594
0.1516
17.38
0.6033
0.2433
16.08
0.4525
0.2439
4DGS
19.54
0.7723
0.1744
18.13
0.6577
0.1706
21.01
0.8016
0.1534
16.17
0.5384
0.2431
MotionGS
14.59
0.4542
0.3284
12.43
0.3155
0,3460
13.95
0.3903
0.3366
10.47
0.2114
0.4195
NT
29.9
0.9518
0.0488
22.34
0,7912
0.09
28.26
0.9453
0.D461
16.7
0.5584
0,1974
HDR- Hexplane
18.84
0.6643
0.2116
19.82
0.6863
0.1631
18.38
0.6246
0,2101
19.3
0.5772
0.1992
32.33
0.9522
0.0493
27.13
0.8037
0.0603
33.11
0.3422
0.0473
26.22
0.8431
0.0827
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.
C.4 OBJECTIVE FUNCTION
Finally, our HDR-NSFF end-to-end optimized using the following loss:
L L= + Lphoco + Beata + Preg + Cmmooth + Beah
(27)
where the 3 coefficients weight each term. Additional regularization terms, Leeg leveraging scene flow priors.
D ADDITIONAL EXPERIMENT RESULTS
We provide additional experimental results that could not be included in the main manuscript, due
to page limit. Specifically, Tables [S2] S3 & [S4] present per-scene quantitative results for each
experiment. Figures 58 ST9 illustrates qualitative outcomes for additional real datasets not shown in
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Big jump
Side walk
Full
Dynamic only
Full
Dynamic only
Mothod
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
13.49
0.609
0.2292
HDR-Hoxplaaee
19.01
0,5076
0.2301
15.13
0.4166
0,2604
19.08
0.4914
0
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 juck
Poinung walk
Full
Dynamic only
Fall
Dynamic only
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSFF
18.26
0.7873
0.1212
19.82
0.62
0.1448
18.21
0.7752
0.1286
13.56
0.6301
0.248
HDR Hexplane
17.22
0.4626
0.2134
19.56
0.5833
0.1471
16.98
0.4383
0.2107
16.4
0.6171
0.1702
Ours
32.03
03457
0.0355
24.2
0.7954
0.0955
30.05
0.934
0.0473
20.74
0.7194
0.1287
Tube loss
Bear Uhnea
Method
Full
Dynamic only
Fall
Dynamic only
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSFF
18.5
0.7968
0.1095
18.4
0.7628
0.0967
16.91
0.5648
0.4884
15.96
0.3872
0.5795
HDR-Hexplane
17.15
0.4737
0.2204
16.35
0.6457
0.96
21.81
0,785
0,2604
17.35
0.493
0.3452
Ours
32.27
0.9482
0.0378
25.19
0.9125
0,0498
30.4
0.9235
0.1437
24.29
0.7626
0,2869
Dog
Tumbler
Foll
Dynamic only
Full
Dynamic only
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSFF
18,42
0.5972
0.3588
15.28
0.4708
0.3672
18.84
0.6352
0,2457
16.25
0.4823
0.3693
HDR- Hexplanee
25.02
0.8438
0.1881
17.38
0.4989
0,2992
24.93
0.8247
0.1837
20.42
0.6685
0.2142
Ours
33:45
0.9452
0.0875
21.36
0.6793
0.2781
35.14
0.944
0.0752
26.12
0.8476
0.1415
Fire extinguisher
Laptop
Full
Dynamic only
Fall
Dynamic only
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSEF
22.94
0.8639
0.1094
27.67
0.6782
0.1842
24.19
0.8908
0.0760
28.87
0.7801
0.0971
HDR Hexplane
23.56
0.8854
0.0804
27.84
0.7416
0.0847
23.54
0.8992
0.0719
29.67
0,8359
0.0610
Ours
37,00
0.9694
0.0331
32.46
0.8568
0.0984
37.41
0.9763
0.0237
33.72
03187
0,0454
Bag
Ball
Full
Dynamic only
Dynamic only
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
NSFF
19.33
0.7352
0.1815
20.51
0.6336
0.1439
17.95
0.6685
0,2042
17.25
0.5172
0.2595
HDR-HE Hexplae
18.85
0.6641
0.2116
19.76
0.6818
0.1627
18.36
0.6243
0,2104
18.91
0.5684
0.2014
Ours
32.26
0.9525
0.0525
26.91
0.8873
0.0748
32.64
0.9468
0.0499
25.25
0.8185
0.1105
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.
synthetic dataset-Fuu Fulll
synthetic dataset- dataset-Dynamia Dynamic
Methods
Lego
Mutant
Standup
Jumping Jack
Methods
Lego
Mutant
Standup PSNR
Jumping Jack
PSNR
NSFF
13.45
16.97
13.47
15.33
NSFF
13.94
18.43
10.25
13.74
NeRF-WT
29.55
33,06
32.55
29.25
Ne
22.32
27.58
19.77
16.33
HDR-Hexplame
28.58
30.88
30.83
29.50
HDR-Hexplane
24.61
29.71
21.39
19.57
Ours
34.64
36.13
35.80
33.72
Ours
28.77
31.80
24.98
23.21
SSIM
SSIM
NSFF
0.6472
0.6348
0.4958
0.6551
NSFF
0.6145
0.3152
0.1601
0.5795
NeRF-WT
0.9595
0.9114
0.9556
0,9200
NeRF-WT
0.8517
0.8289
0.7741
0.5412
HDR-Hexplane
0.9443
0.8526
0.9112
0,9137
HDR-Hexplane
0.8626
0,8443
0.7665
0.7262
Ours
0,9670
0.9278
0.9564
0.9348
Ours
0.9062
0.9115
0.8816
0.8349
LPIPS
LPIPS
NSFF
0.1556
0.1243
0.2368
0.1364
NSFF
0.1528
0.1708
0.3097
0,1345
NeRF-WT
0.0171
0.0316
0.0224
0.0655
NT
0.0592
0.0845
0.0988
0.1
HDR Hexplane
0.0257
0.0708
0.0603
0.0539
HDR Hexplane
0.1217
0.0724
0.1547
0.0794
Ours
0.0147
0.0305
0.0249
0.1229
Ours
0.0426
0.0590
0.0749
0.0538
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.
the main paper. Moreover, supplementary videos include more HDR, LDR, and novel view rendering results. Please refer supplementary video for further visualization results.
D.1 ABLATION STUDY
We analyze the impact of our proposed semantic-based optical flow on the novel view synthesis task using 8 real dataset samples. We compare two variants of our method: (1) Ours (w/ RAFT), in which the RAFT optical flow is used without modification, and (2) Ours (w/ RAFT Finetuned). where RAFT is fine-tuned on synthetic multi multi-exposure data. Note that, as shown in Figure the original RAFT model was not trained on multi-exposed images, resulting in high errors when applied directly in our
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motion cues Published as a conference paper at ICLR 2026
Full
Dynamic only
Methods
PSNR1
SSIM
LPIPS
PSNR1
SSIM
LPIPS]
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
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 and Deng (2220) and a stronger bascline fine-tuned RAFT on multi-exposure adaptation of the Flying Th- ings3D dataset.
Full
Dynamic only
Methods
PSNR
SSIM
LPIPS,
PSNR
SSIM
LPIPS,
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
Table S6: Quantitative results of MoSca with our proposed modules on GoPro outdoor scenes for HDR novel view synthesis. MoSca with Tone-mape conemmaping (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.
setting, By fine-tuning it synthetic data, the performance is improved. As shown in Table S5 proposed method achieves the best results.
E EXTENSION TO 3DGS-BASED DYNAMIC SCENE RECONSTRUCTION
While our method is developed on top of NSFF, its core pampoonnts--toe--mpppe robust semantic flow via DINO- Tracker not tied to the NSFF framework. In this section, we demonstrate that our approach is broadly applicable and can be seamlessly integrated into 3D Gaussian Splatting (3DGS)-bare bassed dynamic reconstruction pipelines.
and exposure-
Applicability beyond NSFF. The primary objective of our design is to make dynamic 4D recon- struction robust to the exposure variance inherent in elternating-eppouuuee exposure monocular videos. To verify that the proposed components are beehhd-aanosttce we extend our pipeline to a representative 3DGS-based method, MoSca (Lei et al. 2025). For this purpose, MoSca without modifying its core architecture. First, our learnable tone-mapping (TM) module transforms LDR frames acquired under varying exposures into a unified HDR radiance space, provid- ing Crposure-invariant appearance supervision throughout the optimization. In addition, we replace MoSca's original motion estimation, CoTracker (Karaev al. 2024) with semantic flow obtained from DINO- Tracker(DT), which offers robust correspondence cues under severe illumination and exposure variations. With these modifications. we assess whether the proposed components can still enhance HDR dynamic reconstruction in a framework that depends on explicit Gaussian tracking rather than continuous scene-flow modeling.
integrate our modules into
Experimental setup. We run all experiments on the proposed GoPro outdoor dataset. For this ablation, we compare two variants of MoSca augmented with our components: MoSea TTM, which incorporates only our tone-mapping (TM) module, and MoSca TM + DT, our full configuration equipped with both exposure-aware appearance normalization and robust semantic flow (DINO- Tracker, DT). We note that the original MoSca configuration with CoTracker-baaee consistently failed to converge under alternating-exposur inputs. Thus, we exclude it from compari- son. We evaluate novel view synthesis (NVS) results and report quantitative metrics in addition to qualitative visualizations.
Quantitative results. Table S6 that applying only the tone-mapping module improves the HDR appearance reconstruction, as successfully aligns exposure-varies images into a common radiomet- ric domain. However, this configuration struggles to recover consistent motion: the CoTracker-baan based correspondences often fail under extreme exposure variations, leading to incorrect dynamic geometry. When both TM and DINO-Trackes applied, the model achieves the best performance across all
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MoSca w/ TM
MoSca w/ TM + DT
MoSea wi TM
MoSca w/ TM + DT
MoSca w/ TM
MoSea w/ TM - DT
Big. jump
Jumping jack
Tube loss
Pointing walk
Side walk
Figure $4: Qualitative results of MoSca with our proposed modules 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 MNNcker (DT) leverages exposure- robust semantic flow, yielding stable geometry and photometrically consistent HDR novel views under challenging exposure alternation.
metrics. The semantic flow provides exposure-robust motion cues, enabling the 3DGS optimization to recover temporally stable Gaussian trajectories and coherent HDR appearance over time.
Qualitative results. Figure S4 shows qualitative results of MoSca based our approach on our GoPro Outdoor scenes. While MoSca+ TM produces HDR-like frames, the reconstructed geometry becomes inconsistent across time due to unreliable motion supervision. In contrast, MoSca+TM+DT produces stable and photometrically consistent HDR novel views, successfully handling complex exposure alternation and large dynamic motions.
These results demonstrate that the improvements brought by our approach-our tone-mapping module and exposure-robust semantic flow-are not specific to NSFF. The same components substan- tially enhance a 3DGS-basse method, improving both radiance reconstruction and motion stability. This indicates that the proposed modules provide general utility for 4D HDR reconstruction from alternating-exposet monocular videos, regardless of the underlying 3D representation.
F 2D-TO-4D HDR RECONSTURCTION
We compare our method against two-stage baseline that first reconstructs an HDR video from alternating-eppoure input frames using existing 2D HDR video
(Chung and Cho 2023), HDRFlow (Xu al., 2024). and NECHDR (Cui et al. (024) and subsequently applies dynamic 4D reconstruction framework. For each method, the predicted HDR frames tone-mapped using a fixed lllaw operator, which compresses HDR radiance values into the LDR domain through a logarithmic mapping:
M(E) log(1 + FE)
(28)
log(1 + 14)
where μ 500 controls the compression strength. The tone-mapped frames are then provided as input to MoSca (Lei et al. 2025) for 4D reconstruction. For a fair comparison with the two-stage 2D-to-4D baseline, which uses MoSca for 4D reconstruction, additionally report MoSca-based variant our pipeline HIDR-MoScal) This isolates the effect of the 2D HDR reconstruction stage from the choice of 4D representation.
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Published as a conference paper at ICLR 2026
Ours rendered view
LAN-HDR
HDRFloW
NECHDR
HDR-MoSca
Ours
Figure S5: Qualitative comparison with 2D-to-4D HDR reconstruction. A sto-stage baseline reconstructs HDR video using LAN-HDR (Chung and Cho 2023). HDRFlow (Xu et al. 2024), or NECHDR (Cui al. 2024), applies fixed μı-law tone mapping, and performs 4D reconstruction with MoSea (Lei et al. 2025). For reference, we also include 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.
While these 2D HDR video methods produce visually plausible results under mild motion, they fundamentally operate within the 2D image plane and therefore inherit the limitations outlined in Figure In scenarios with noticeable camera motion they struggle reliably handle occlusions, complex dynamics, and the severe exposure inconsistency intrinsic to alternating-exposure videos.
When such inconsistent HDR frames used for 4D reconstruction, the downstream model receives observations that are radiometrically unstable and geometrically incoherent, preventing reliable estimation of density, radiance, and dynamic motion. Since the second stage has no mechanism to correct errors originating from the 2D reconstruction stage, these radiometric inconsistencies propagate forward and degrade the overall quality of the 4D reconstruction. Figure S5 shows that the two-stage 2D-to-4D baseline often produces temporally inconsistent radiance fields and fails to reconstruct stable geometry under exposure variation.
In contrast, our method performs end-to-end dynamic HDR radiance reconstruction directly from alternating-eeppsurr inputs, jointly reasoning about radiance, geometry, and motion within a unified 4D representation. This formulation leverages geometric and motion priors unavailable to 2D methods, enabling consistent tone reproduction, recovery of valid information in saturated regions, and significantly more stable reconstruction under challenging exposure variations.
G QUALITATIVE RESULTS ON HDR-GOPRO DATASET
Figure S6 and show training view and ours reconstructed views HDR-GoPro dataset. Figure S8 S19 show qualitative results novel view synthesis on HDR-GoPro dataset.
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Published as a conference paper at ICLR 2026
Low
High
Mid
Input LDR
Bear thread
Ours HDR
Input LDR
Tumbler
Ours HDR
PROUCE
Input LDR
Dog
Ours HDR
Figure S6: Training view and ours reconstreuted views on GoPro indoor dataset. The odd- numbered rows show sample LDR frames from the input sequence, captured with alternating expo- sures (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 expected, such the smile-emoji picture on the wall 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.
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Published as a conference paper at ICLR 2026
Low
High
Mid
Input LDR
Big jump
Ours HDR
Input LDR
Jumping jack
Ours HDR
Input LDR
Pointing walk
Ours HDR
Input LDR
Side walk
Ours HDR
Input LDR
Tube toss
Ours HDR
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 expo- sures (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 recon- structs 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 wide range of exposure conditions.
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Published as a conference paper at ICLR 2026
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LDR
GT
LDR HDR-Hexplane
LDR Ours
TOOSS TONTRR
A8BA HOUKE
NeRF-WT WT
HDR HDR-Hexplane
HDR Ours
Tumbler
Figure S8: Qualitative results of novel view synthesis Tumbler data.
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Published as a conference paper at ICLR 2026
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LDR
GT
LDR HDR-Hexplane
LDR Ours
NeRF-Wi WT
HDR Hex-Haee
HDR Ours
Bear thread
Figure S9: Qualitative results of novel view synthesis on Bear thread data.
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Published as a conference paper at ICLR 2026
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LDR
GT
LDR HDR-Hexplane
LDR Ours
NeRF-WT WT
HDR HDR-Hexplane
HDR Ours
Dog
Figure S10: Qualitative results of novel view synthesis Dog data.
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Published as a conference paper at ICLR 2026
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LDR
GT
LDR HDR-Hexplane
LDR
Ours
NeRF-WT
HDR HDR-Hexplane
HDR Ours
Big jump
Figure $11: Qualitative results of novel view synthesis on Big jump data.
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Published as a conference paper at ICLR 2026
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LDR GT
LDR HDR-Hexplane
LDR
Ours
NeRF-WT
HDR HDR-Hexplane
HDR Ours
Jumping jack
Figure S12: Qualitative results of novel view synthesis on Jumping.jee jack data.
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Published as a conference paper at ICLR 2026
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LDR
GT
LDR HDR-Hexplane
LDR
Ours
NeRF-WT
HDR HDR- Hie-HHeplaane
HDR
Ours
Figure S13 Qualitative results of novel view synthesis on Pointing walk data.
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Published as a conference paper at ICLR 2026
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Time: 28, View: 3
Time: 28, View:
LDR
GT
LDR HDR-Hexplane
LDR
Ours
NeRF-WT WT
HDR-Hexplane
HDR
HDR Ours
Side walk
Figure S14: Qualitative results of novel view synthesis Side walk data.
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Published as a conference paper at ICLR 2026
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LDR
GT
LDR HDR-Hexplane
LDR
Ours
NeRF-WT
HDR HeR-Hexplane
HDR Ours
Tube toss
Figure S15: Qualitative results of novel view synthesis on Tube toss data.
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Published as a conference paper at ICLR 2026
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LDR GT
LDR HDRR-Heppane
LDR
Ours
NeRF-WT
HDR HDR-Hexpllne
HDR Ours
Fire extinguisher
Figure S16: Qualitative results of novel view synthesis Fire extinguisher data.
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Published as a conference paper at ICLR 2026
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LDR GT
HIDR-HOO Hedeexppen
LDR
HDR
LDR
Ours
NeRF-WT
HDR-HHexpan
HDR
HDR Ours
Laptop
Figure $17: Qualitative results of novel view synthesis Laptop data.
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Published as a conference paper at ICLR 2026
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Time: 20, View: 9
LDR GT
LDR HDR-Hexplane
LDR
Ours
NeRF-WT
HDR HDR-Hexplane
HDR Ours
Bag
Figure S18: Qualitative results of novel view synthesis Bag data.
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Published as a conference paper at ICLR 2026
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LDR GT
LDR HDR-Hexplane
LDR
Ours
NeRF-WT
HDR HDR-Hexplane
HDR Ours
Ball
Figure S19: Qualitative results of novel view synthesis Ball data.
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Published as a conference paper at ICLR 2026
USE OF LARGE LANGUAGE MODELS
large language model was used only for minor assistance in writing and improving the clarity of presentation.
39


Reviews:
[{"review_id": "AdKPxXclLg", "reviewer": "Reviewer_gRTD", "summary": "This paper proposes HDR-NSFF, which reconstructs an implicit 4D scene representation from a monocular dynamic scene video with random exposures, enabling the rendering of HDR novel views or controllable LDR results. The method leverages DINO-Tracker to enhance the robustness of optical flow against illumination variations and incorporates generative priors with multi-view information to address the ill-posed nature of monocular video reconstruction. Experimental results demonstrate the effectiveness of the proposed method compared to dynamic NeRF and 4DGS approaches when handling input videos with random exposures.", "questions": "In most 2D HDR methods, the scenes being processed involve small or even fixed camera motions. How does the proposed method perform on the commonly used 2D HDR datasets under such conditions?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "- The paper focuses on novel view synthesis for dynamic scenes with randomly exposed inputs, which is a relatively unexplored area in the current novel view synthesis field and holds considerable research value.\n- The pipeline of the proposed method is concise and effective, and the introduced Robust Learning Strategies effectively mitigate the sensitivity of the optical flow prior to variations in input exposure.\n- Experimental results demonstrate that the proposed method can handle multi-exposure input videos more effectively than dynamic NeRF and 4DGS methods.", "weaknesses": "1. The proposed method is built upon the NSFF framework, which relies on COLMAP to obtain camera parameters from the input images. However, when the input video exhibits large exposure variations, the quality of the camera parameters estimated by COLMAP may degrade or even cause COLMAP to fail entirely. The paper does not provide a clear solution to this issue, which may limit the practical applicability of the proposed method.\n2. The paper points out that modeling HDR in 3D scenes can effectively alleviate the exposure inconsistency problem found in 2D methods. However, in the 2D HDR domain, NECHDR[1] specifically addresses the issue of exposure inconsistency in 2D approaches. Therefore, the authors should take the NECHDR work into consideration when performing HDR rendering on training views, and demonstrate through experiments that the proposed method outperforms NECHDR in terms of exposure consistency, or, if the exposure consistency performance is comparable, that the proposed method offers advantages in other aspects such as handling occlusions. \n3. The proposed method should be compared with an additional pipeline: first, applying a 2D single-image or video-based HDR algorithm to the input multi-exposure video to obtain HDR results, and then using existing dynamic NeRF or 4DGS methods to represent the 4D scene based on the generated HDR video.\n4. How is the ablation study in Table 3 designed? Both Dino-Tracker and the generative prior are supposed to be the key technical contributions of this paper. Why is there an entry labeled “Ours with Dino-Tracker”—does this imply that the main Ours method does not use Dino-Tracker? The authors should clearly explain the design of the ablation study. It is also recommended to conduct ablation experiments on the full model by individually removing modules such as Dino-Tracker or the generative prior. Moreover, Figure S3 in the supplementary material shows that the improvement brought by the generative prior (GP) to the visual results appears to be minimal, as the visualizations before and after adding GP look almost identical. The authors should clarify the reason for this observation.\n\n[1] Exposure Completing for Temporally Consistent Neural High Dynamic Range Video Rendering. ACMMM 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "s7utafka6O", "reviewer": "Reviewer_nGFz", "summary": "This paper presents HDR-NSFF, a method that builds a high dynamic range random field of dynamic scenes. It proposes a framework that incorporates a learnable tone-mapping to adapt to high dynamic range. It also collects an HDR dynamic scene dataset for the task.", "questions": "See above.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. It proposes a learnable tone-mapping function for HDR reconstruction.\n\n2. It introduces a new HDR dynamic scene dataset.\n\n3. Experimental results are better than other SOTA methods.", "weaknesses": "1. I'm wondering why this paper is conducted on NSFF methods, as there are many advanced methods in NeRFs and Gaussians in recent years. Using an outdated baseline somehow weakens the contributions of the paper.\n\n2. The writing of the method section includes so many details on pretrained model selections, whereas the main contribution about the dataset and the learnable tone mapping is relatively introduced far behind. This may mislead the reader's judgment of an important part of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FiGVD7uNZE", "reviewer": "Reviewer_MPxi", "summary": "This paper presents HDR-NSFF, a novel framework for reconstructing dynamic HDR radiance fields from alternatively exposed monocular videos. The approach integrates 3D scene flow estimation, HDR radiance reconstruction, and tone mapping into a unified end-to-end framework. The authors also introduce a real-world GoPro dataset with synchronized multi-exposure captures to support systematic evaluation.", "questions": "1. Are the nine cameras perfectly parallel to each other, or are they converging toward a common focal point?\n\n2. Were all cameras fixed and synchronized during the capturing process?\n\n3. The proposed framework involves multiple loss terms. Is the optimization process stable, and are the chosen hyperparameters suitable for all scenes?\n\n4. Since the method adopts a per-scene optimization strategy to reconstruct dynamic HDR radiance fields, can it generalize to render arbitrary viewpoints and timestamps?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is well-structured, and the experiments are fairly comprehensive. Reconstructing dynamic HDR radiance fields is also an interesting and meaningful idea.", "weaknesses": "1. The visual quality of the reconstructed images is not satisfactory; noticeable blurriness can be observed in several results (e.g., Figure 1 and Figure 6). Could the authors provide an explanation for this issue?\n\n2. Long exposure tends to introduce motion blur (e.g., in the Big Jump scene), while short exposure often leads to noise. How do the authors address this trade-off in their method?\n\n3. When comparing with other HDR reconstruction methods, was the same tone-mapping function used for visualization and evaluation?\n\n4. How are the flow and depth representations integrated into the proposed system?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QWzBjjJhUH", "reviewer": "Reviewer_Jfnv", "summary": "Paper proposes HDR-NSFF for reconstructing dynamic High Dynamic Range (HDR) radiance fields from alternatively exposed monocular videos. ​ Unlike traditional HDR methods operating on 2D images, HDR-NSFF models 3D scene flow, HDR radiance, and tone mapping in a unified end-to-end pipeline, which incorporates exposure-robust semantic flow estimation using DINO features, robust depth estimation, and generative priors to address challenges like color inconsistencies and information loss due to saturation. ​ The method is valuated on real-world and synthetic datasets, showing its outperformance over state-of-the-art methods in novel view synthesis, time interpolation, and combined view-time synthesis, achieving superior reconstruction quality and temporal coherence. ​", "questions": "Note to authors: my preliminary rating would’ve been more like 5 (currently the next rating from 4 is 6) which will be finalized during/after rebuttal. \n\n1. Have you explored strategies to optimize the training process or reduce the computational cost for real-world applications? ​\n\n2. How do you ensure that the generative priors do not introduce significant artifacts or hallucinations during the reconstruction process? ​ Have you evaluated the impact of these priors on the overall fidelity of the HDR radiance fields? ​\n\n3. How well does the model generalize to other real-world scenarios with more drastic lighting conditions, object types, and motion patterns? E.g. indoor rave party scene with laser and abrupt lighting changes with multiple dancers in complex motions under full and partial occlusion.\n\n4. How do you address inaccuracies propagated by DINOv2 and Depth-AnythingV2?\n\n5. What are the prospects for adapting HDR-NSFF for real-time applications, such as live video processing or interactive systems?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "1. HDR-NSFF explicitly models 3D scene flow, enabling robust handling of occlusions, large motions, and dynamic changes in geometry and radiance. ​The use of DINOv2-based semantic features for optical flow estimation enhances robustness to exposure variations, addressing a key challenge in HDR reconstruction under large motion. ​\n\n2. The proposed generative priors compensate for information loss due to saturation and sparse-view limitations. ​\n\n3. A real-world GoPro dataset is introduced with synchronized multi-exposure captures. ​", "weaknesses": "1. The problem setting, capture (alternative exposures), and employment of generative methods for HDR were explored previously or similarly or at least a straightforward extension in hindsight to the current dynamic HDR for neural radiance fields. \n\n2. The GoPro dataset may not adequately capture the diversity of real-world lighting conditions, object types, and extreme motion scenarios, as the examples demonstrated in this paper involves simple (but large motions) and often times sunny outdoor scenes, where HDR may not be  ecessarynto reveal lost details due to saturation and under exposure with noise corruption. \n\n3. Generative priors may introduce artifacts or hallucinations, potentially affecting the fidelity of the reconstruction. ​Please point out if current experiments address this issue. \n\n4. The framework requires significant training time (10+ hours per scene). ​", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)