High Dynamic Range Novel View Synthesis with Single Exposure
Authors: Kaixuan Zhang
HuWang
Minxian Li
Mingwu Ren
Mao Ye
Xiatian Zhu
Venue: ICML 2025 poster (2025)
Keywords: High Dynamic Range
Novel View Synthesis
Low Dynamic Range
Data Synthesis
Primary area: deep_learning->algorithms

Abstract: High Dynamic Range Novel View Synthesis (HDR-NVS) aims to establish a 3D scene HDR model from Low Dynamic Range (LDR) imagery. Typically, multiple-exposure LDR images are employed to capture a wider range of brightness levels in a scene, as a single LDR image cannot represent both the brightest and darkest regions simultaneously. While effective, this multiple-exposure HDR-NVS approach has significant limitations, including susceptibility to motion artifacts (e.g., ghosting and blurring), high capture and storage costs.
To overcome these challenges, we introduce, for the first time, the  single-exposure HDR-NVS problem, where only single exposure LDR images are available during training. We further introduce a novel approach, Mono-HDR-3D, featuring two dedicated modules formulated by the LDR image formation principles, one for converting LDR colors to HDR counterparts, and the other for transforming HDR images to LDR format so that unsupervised learning is enabled in a closed loop. Designed as a meta-algorithm, our approach can be seamlessly integrated with existing NVS models. Extensive experiments show that Mono-HDR-3D significantly outperforms previous methods.
Source code is released at https://github.com/prinasi/Mono-HDR-3D.

High Dynamic Range Novel View Synthesis with Single Exposure
Kaixwan Zhang
Hu Wang² Minxian Li
Mingwu Ren 13
Mao Ye? Xiatian Zhu"
Abstract
At 0.125s
High Dynamic Range Novel View Synthesis (HDR-NVS) aims to establish a 3D scene HDR model from Low Dynamic Range (LDR) im- agery. Typically, multiple-exposure LDR im- ages are employed to capture a wider range of brightness levels in scene, as single LDR im- age cannot represent both the brightest and dark- est regions simultaneously. While effective, this multiple-exposeee HDR-NVS approach has sig- nificant limitations, including susceptibility to mo- tion artifacts (e.g., ghosting and blurring), high capture and storage costs. To overcome these challenges, introduce, for the first time, the single-exposure HDR-NVS problem, where only single exposure LDR images are available during training. We further introduce a novel approach, Mono-HDR-3D, featuring two dedicated modules formulated by the LDR image formation prin- ciples, one for converting LDR colors HDR counterparts and the other for transforming HDR images to LDR format so that unsupervised learn- ing is enabled in a closed loop. Designed as a meta-algorithm,, our approach can be seamlessly integrated with existing NVS models. Extensive experiments show that Mono-HDR-3h signifi- cantly outperforms previous methods. Source code is released at https:/ / github. com/ prinasi/Mono-io http//Mooooo---------
(b)
32s
Figure 1. Examples of (a, b) underexposure and (c, d) overexpo-
sure. At: Exposure time.
appealing representations of 2024). It can encompass both the darkest shadows and the brightest highlights within a single frame. This capability is crucial in a number of fields such as creative media pro- duction, photography, virtual reality, and augmented reality that require precise color reproduction, detailed shadow and highlight information, and enhanced visual realism (Wang & Yoon, 2021). This enhanced dynamic range not only facil- itates more realistic and visually appealing representations of complex scenes (Liu et al., 2023), but also improves the performance of various computer vision tasks, such as ob- ject recognition, scene segmentation, and depth estimation, by providing richer and more detailed visual information (Yan et al., 2023).
scenes (Cai et al.,
1. Introduction
Compared to common Low Dynamic Range (LDR) imaging, High Dynamic Range (HDR) imaging enables the capture and representation of a broader range of luminance/ bright- ness levels, thereby providing more realistic and visually
'Nanjing University of Science and Technology. Universiiy of Electronic Science and Technology of China. "State Key Lab- oratory of Intelligent Manufacturing of Advanced Construction Machinery. *University of Surrey. Correspondence to: Minxian Li uminxianli@njjsst.dde
Novel View Synthesis (NVS) refers to the process of gener- ating new views of scene from arbitrary viewpoints, given set of input images captured from different perspectives (Duan et al., 2024). This involves understanding and mod- cling the underlying 3D structure of a scene, as well as accurately rendering the appearance of the scene and ob-
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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High Dynamic Range Novel View Synthesis with Single Exposure
jects. Most NVS works focus on LDR image models which fall short in those domains requiring HDR rendering.
the challenging single exposure scenario. Designed a generic approach, our method can be integrated with differ- ent 3D scene models such as NeRF (Mildenhall et al., 2021) or 3D Gaussian Splatting (3DGS) (Kerbl et al., 2023). (III) Extensive experiments validate that our method significantly outperforms previous alternatives.
Indeed, a couple of recent works (Cai et al., 2024; Huang et al., 2022) have studied the HDR-NVS problem by captur- ing multiple exposures of LDR images per view about the same scene. However, multiple exposure-based approaches remain vulnerable to motion artifacts, ghosting effects, and demand precise alignment of images captured under vary- ing exposure settings (Eilertsen et al., 20) Specifically, longer exposure frames tend to accumulate object or cam- era movement, leading to blurred details-an an issue that becomes more pronounced when exposure durations differ significantly (Kalantari et al., 2017). During HDR synthe- sis, pixel-wise fusion (e.g., weighted averaging) can su- perimpose differing object positions onto the same region, producing semi-transparent or duplicated contours that are especially evident in dynamic scenes with significant object displacement (Reinhard, 2020). Furthermore, variations in exposure times often yield discrepancies in brightness distri- bution, local contrast, and overall appearance, complicating conventional registration algorithms. Finally, in rapidly changing environments or when using mobile devices, cap- turing multiple exposures in quick succession may prove impractical, limiting the applicability of these methods.
2. Related Work
High Dynamic Range Imaging Conventional HDR imag- ing primarily relies on specialized high-end cameras to cap- ture HDR images (Tiwari & Rani, 2015). However, the high cost of these cameras renders them inaccessible to general consumers. An alternative approach involves reconstructing HDR images from imagery captured by LDR cameras using algorithms (Wang & Yoon, 2021). Before NeRF (Milden- hall et al., 2021) was proposed, two primary approaches have been extensively explored. The first generates HDR content by merging multiple LDR images of the same scene taken at varying exposure levels (Kalantari et al., 2017: Yan et al., 2020). However, the necessity of capturing LDR images with different exposures demands specific software and hardware capabilities, which is not only costly but also brings in various issues, as discussed earlier. Thus, the second focuses on synthesizing HDR imagery from single- exposure LDR images (Eilertsen et al., 2017 Without the challenges associated with multi-exposure capture, it is more feasible for generating HDR images in scenarios where multiple exposures are impractical or datasets are limited (Hanji et al., 2022).
To address these issues, we propose a more deployable yet more challenging task, namely HDR-NVS with single- exposure LDR images, which climinates the reliance on mul- tiple exposures and thus avoids the aforementioned limita- tions. However, as illustrated in Fig. 1, ages frequently suffer from overexposure or underexposure, posing significant challenges for HDR-NVS. Furthermore, we present novel HDR 3D scene modeling framework, Mono-HDR-3D, characterized by learning to approximate the underlying LDR image formation process of camera imaging. Specifically, we start with learning an LDR 3D scene model from single-exposure LDR training images, followed by lifting the color space to HDR with dedicated color transformation module in a per-channel manner. This Hom---DR-to-HDD design is opposite to previous models since single-exposure LDR images are insufficient for de- riving an HDR model. We further introduce a closed-loop design by augmenting a process of converting HDR images to LDR images, allowing additional supervision even in the case of no access HDR ground-truth training data.
im-
Recent deep learning based methods try capture the map- ping relationship between LDR and HDR images, often achieving ofate-of-the-aat performances (Dille et al., 2025; Kim et al., 2024). However, these methods mainly focus on individual 2D imagery, lacking 3D perception capabil- ities and are not suitable for the novel view HDR image rendering problem.
Novel View Synthesis (NVS) is essential for applications such as virtual/augmented reality, gaming, and 3D recon- struction (Avidan & Shashua, 1997; Gao et al., 2023). Tra- ditional methods, including Structure-from Mrot-MMoon (SfM) (Schonberger & Frahm, 2016) and Multi- Sterco (MVS) (Rosu & Behnke, 2022), rely on multi-view ge- ometry to reconstruct 3D scenes but often struggle with occlusions, textureless regions, and high computational costs (Jiang, 2023). Recent advances in NVS leverage deep learning to learn continuous scene representations. Notably, NeRF (Mildenhall et al., 2021) encodes color and density in a neural network, enabling novel view synthesis by querying 3D coordinates and viewing directions. Extensions such as Mip-NeRF (Barron et al., 2021), FastNeRF (Garbin et al., 2021), and transformer-basee models (Lin et al., 2023; Miy- ato et al., 2023) have improved efficiency, scalability, and
Our contributions can be summarized as follows. (I) We introduce a new HDR-NVS problem where only single- exposure LDR images are needed so that the data acquisi- tion process is made significantly easier and generic, as well as eliminating those intrinsic limitations with multiple expo- sures. (II) We propose generic framework, Mono-HDR- 3D, that learns to capture the underlying camera imaging process for bridging LDR and HDR space effectively under
2

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High Dynamic Range Novel View Synthesis with Single Exposure
L2H1-CC
L2H-CC
L2H-CC
HDR Image 1
Viewpoint V Rasterization
Training
Iteration
LDR Scene Model
HDR 3D Scene Model
H2L-CC
Viewpoint V
Rasterization
Single Exposure LDR Views with Poses
LDR Image 1!
LDR Image I'
(a)
(b)
(c)
Figure 2. Overview of Mono- Mono-HDR-33 HDR-3D. (a) Given single exposure LDR training images with camera poses, we learn an LDR 3D scene model (e.g., NeRF or 3DGS). (b) Importantly, this LDR model is lifted up HDR counterpart camera imaging aware LDR-to- HDR Color Converter (L2H-CC). (c) Further, closed-loop design is formed by converting HDR images back to LDR counterparts with latent HDR-to-LDR Color Converter (H2L-CC). This enables optimizing useful in case of no access to HDR training data. During inference, only the HDR or I.DR 3D scene model is needed, taking the novel camera view as the input and outputting the corresponding image rendering.
HDR model even with LDR training images, particularly
quality. However, most techniques focus on LDR outputs, limiting their applicability for HDR rendering. Alterna- tively, 3D Gaussian Splatting (3DGS) represents scenes using learnable 3D Gaussians optimized with multi-view supervision (Kerbl et al., 2023) which bypasses volumet- ric integration and heavy network optimizations, achieving faster training and inference, facilitating real-time rendering-
3. Method
3.1. Problem formulation
For each of N distinct viewpoints V {V1, V2,... Vx). we capture a set of single-exposure LDR images denoted as IV {IVy Iy....... I J. The objective is to learn a 3D scene model FF that synthesize HDR image I V..... for any given novel viewpoint Vnew:
HDR Novel View Synthesis (HDR-NVS) aims to generate novel view HDR images from LDR observations, crucial for scenes with large brightness variations and rich details. Huang et al. proposed the first HRD-NVS model, HDR- NeRF (Huang et al., 2022), by extending standard NeRF (Mildenhall al., 2021) to learn implicit mapping from physical radiance HDR color. However, this method is costly in both model training and inference. Taking the advantage of 3DGS (Kerbl et al., 2023), Cai et al. (2024) addressed this issue by learning an MLP-based tone-mapper between LDR and HDR models. Despite their promising re- sults, these methods rely on multiple-exposure LDR training imagery, limiting their applicability cases with dynamic environments or limited image capturing conditions. To address this limitation, we introduce single-exposure HDR- NVS, which leverages only single-exposure LDRR images.
F: (TV V new) + I V.ew
(1)
The synthesized HDR image I V.... needs exhibit an ex- panded dynamic range compared LDR training imagery, while maintaining geometric coherence with the underlying 3D structure of the scene (Reinhard, 2020). Let G represent the 3D geometry inferred from I', then F(I'y, Vnew) must align with G at a viewpoint Vnew- addition, the HDR synthesis must preserve consistent lighting and color across different views (Debevec & Malik, 2023).
Formally, we need to ensure the following constraint holds for each 3D scene point:
C(I) Vnew (Ty. (X)) = (TV, VX G,VV
(2)
where C denotes the color information of images, X repre- sents the 3D point's coordinates, and yv (X) a projection
3

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High Dynamic Range Novel View Synthesis with Single Exposure
function mapping the 3D scene point X onto the 2D image coordinates corresponding viewpoint V.
LO
X
These constraints require the model F to effectively uti- lize the limited information from single-exposure imputs to compensate for the absence of multi-exposure sequences.
LO R
LLO R
SP
LO
3.2. Mono-HDR-3D
Figure 3. Structure of our camera imaging aware LDR-to-HDR Color Converter (L2H-CC). </e": LDR/HDR color; LO: Linear Operation, R: ReLU, SP: Softplus. @ and (:: Element-wise multi- plication and addition.
Architecture. To address the proposed problem as in Sec. 3.1, we propose a novel single-exposure HDR-NVS frame- work, Mono-HDR-3DD Specifically, given single-exposure LDR images (with corresponding camera poses) as input, first learn an LDR 3D scene model (e.g., NeRF (Milden- hall et al., 2021) 3DGS (Kerbl et al., 2023)). This is because single-exposure LDR images provide insufficient information to fully recover an HDR scene. Then, we ele- vate this LDR model to an HDR counterpart via our camera- imaging-awan -aware LDR-to-HDR Color Converter (L2H-CC). Additionally, we introduce a latent HDR-to-LDR Color Con- verter (H2L-CC) as a closed-loop component, enabling the optimization of HDR features even when only LDR training images are available, which ensures the framework to be robust in the absence of ground-truth truth HDR data. The overall architecture of Mono-HDR-3D is depicted in Fig. 2.
where the term (-) is responsible for linearly scaling the brightness values of HDR images to fit within the represen- tation range of LDR images, while the term B(-) is to learn the offset and correction of LDR image brightness values.
By reversing Eq. (5), the HDR value can be obtained
In g/ (4 1o + Loverloww) -t
(6)
X-)
S(-)
Y()
where the term (-) serves as a scaling factor that linearly amplifies the brightness values the LDR image to match the range of HDR images, the term S(-) adjusts and corrects the amplified LDR brightness values, and the term Y() performs noise correction the adjusted HDR brightness values.
Camera imaging mechanism. We embark with the semi- nal LDR image formation formula (Hasinoff et al., 2010):
At/g In + Io tc, Unsaturation;
max,
Saturation
L2H-CC. Simulating the above camera imaging formula Eq. (6), we design an LDR-1o-HDR color converter, L2H- CC, that learns to approximate the inherent camera response characteristics and facilitates accurate HDR color estima- tion:
where Ji denotes the LDR color, At is the exposure time, is the sensor gain, Ln represents the corresponding HDR pixel value, and To is the constant offset current with e de- noting the sensor noise. Unsaturation refers to those pixels that can be accurately represented by the LDR image after the camera's 's imaging pipeline processing, while saturation occurs when the sensor reaches its limit, causing the pixel value to be capped at a maximum saturation value max-
ci fizh (c)),
where ct and cf represent the HDR color and the LDR color, respectively. This is challenging as only the LDR color I/ is known whilst all the rest are not, resulting vast modeling freedom.
Let the saturated pixel value in Eq. (3) of LDR images as
Tmxx
Fideal
Joverflow
(4)
To address this challenge, impose network architectural prior in the spirit of camera imaging. That being said, L2H- CC consists of three dedicated modules organized in a way that approximates the camera's ' color conversion behavior (Eq. (6)), as shown in Fig. 3. Given an LDR color c/. a linear layer with a ReLU activation is first used to convert LDR colors into a latent feature space. To simulate the three terms 5(-), (()) and Y(-), adopt simple MLP with ReLU for efficient non-linear computation. The ReLU activation ensures nonnegative outputs, aligning with the underlying physical constraints of these parameters (Hasi- noff et al., 2010). Note that no activation function is applied to the y-)) module, as the noise component e inherently
where Tideal and Toverflow represent the pixel values captured by an infinitely capable camera and the overflow values between the ideal and real cameras, respectively. For unsat- uration pixels, obviously Lovertiow O.
By integrating Eq. (4) with Eq. (3), the formation process of LDR images can be unified as:
1 = Al/g. To +To + Tovertiow
D(-)
B(-)
4

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High Dynamic Range Novel View Synthesis with Single Exposure
D
Mono-HDR-GS is obtained by integrating Mono-HDR-3D
LO
R
with 3DGS (Kerbl al., 2023). For the LDR branch, adopt:
LO R
SM
1
LO T
B
Lidr Li + CD.SSI I).,
(10)
where the L1 loss and D-SSIM loss (Wang et al., 2004) are balanced by X. i' denotes the ground- truth LDR images, To optimize HDR generation, following HDR-GS use
Figure 4. Structure of our camera imaging aware HDR-to-LDR Color Converter (H2I--CC). I"I HDR/LDR image; I.O: I Linear Operation, R: ReLJU T: Tanh, SM: Sigmoid. <b: Flement-wise addition.
L2 loss in the p-law LDR (Kalantari et al., 2017) domain as
log (op(++)-o))(((")) log (e(1+p--ommi (f) log(I+p)
Lndr =I1
12
random. We also adopt a residual structure (He et al., 2016), which stabilizes the learning process by capturing subtle discrepancies between the LDR input and the HDR output, thereby preserving fine-grained color details more effec- tively.
log(I+1)
(11)
where la denotes the amount of compression, f represents the ground-truth HDR images, and norm(-) specifies the min-ma max normalization.
is formed by integrating Mono-HDR- 3D with NeRF (Mildenhall et al., 2021). In this case, we adopt the Mean Square Error (MSE) based loss function same as HDR-NeRF:
H2L-CC. We further introduce a closed-loop design, H2L- CC, that converts the rendered HDR images back to LDR for enabling HDR model optimization even when only LDR training data is available. Similarly, we formulate this com- ponent according to the camera imaging principle expressed in Eq. (5), formally denoted as:
Lute Lune LazI MSE(I', f))
(12)
=' (Ta)
4. Experiments
where I' and Ih denote the rendered LDR and HDR images, respectively,
Datasets. Following HDR-GS and HDR-NeRF, we use the multi-view image dataset with 8 synthetic scenes created by the software Blender (Blender Foundation, 2025) and real scenes captured by a camera, where each scene con- tains 35 images captured under 5 different exposure times (l2, 12 43: 45,45- We use the same training and test data, where images 18 views with the exposure time randomly selected from {f1, f3, are used for training, while the other 17 views at the same exposure time and HDR images are used for testing. Under our proposed single exposure setting, only a specific exposure time is selected for model training and evaluation for one experiment. All methods are compared fairly using the same training and test sets.
Concretely, H2L-CC is composed of two modules that approximate the terms of Eq. (5), as shown in Fig. 4. We first transform the HDR image colors into latent feature space with linear layer followed by ReLU activation. To simulate each term D(.) and B(.), specific linear layer with activation is employed, with ReLU for D(-) and Tanh for B()) This choice of activation functions is made by their physical meanings (Hasinoff et al., 2010) as discussed earlier, ensuring that the network can effectively simulate the HDR-to-LDR conversion process.
3.3. Model optimization and instantiation
Evaluation metrics. We employ the PSNR and SSIM as quantitative metrics. We utilize LPIPS perceptual met- ric, where lower values signify better perceptual quality. Similar to HDR-GS (Cai et al., 2024), we also quantitatively evaluate the rendered HDR images in the tone- mapped do- main and qualitatively show HDR results tone-mapped by Photomatrix Pro (HDRsoft Team, 2025).
The overall objective loss function of Mono-HDR-3D can be generally expressed as
L = Lidr + aL + 3L
(9)
where Liár denotes the standard loss function of the underly- ing 3D representation model used (e.g., NeRF (Mildenhall et al., 2021) or 3DGS (Kerbl et al., 2023)), Luar for match- ing the HDR ground-truth images if available, and Caz is used to train the proposed H2L-CC in the same function as Lyde. The two pppeer-rraeeerss a and B, control the relative importance among the terms.
Implementation details. Both models trained with the Adam optimizer with the same parameters HDR-NeRF and HDR-GS. For Eq. (9), we set 3 to 0.01/0.05. while a = 0.6 for learning rate of L2H-CC/H2L-CC to 5 x 10 10-4/1 x 10 and the decays to 5 10-5/5 x 10-4 by cross-validation.
HeA-MMMooo-------- HDR-GS. We set the
5

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High Dynamic Range Novel View Synthesis with Single Exposure
Table Quantitative results the synthetic datasets. For the LDR results, we report averaged results across exposure times f1, fa, and la and all scenes.
LDR result
HDR result
Method
Inference Speed (fps)
PSNRT
SSIMT
LPIPS
PSNRT
SSIMT
LPIPS
HDR-NeRF
0.26
30.62
0.658
0.285
13.76
0.511
0.443
Mono-HDR-NeRFF (Ours)
0.26
38.78
0.936
0.048
32.86
0.940
0.068
HDR-GS
147.45
39.48
0.977
0.018
35.30
0.965
0.030
Mono-HDR-GG HDR-GS (Ours)
136.97
41.68
0.983
0.009
38.57
0.975
0.012
0.125s
(8)
Ar
Ar 66622
(c)
HDR-GS
Mono-HDR-GS (Ours)
Ground Truth
Figure 5. Comparison of HDR-NVS on hoth (a/b) synthetic and (c) real datasets, At: Exposure time.
4.1. Quantitative evaluation
results are the most important, as they encapsulate the core objective of this study. The reported results are averaged across three different exposure times to ensure the complete- ness and reliability of the performance metrics.
Competitors. We compare Mono- HDRo- with two latest state-of-the-art approaches: (1) HDR-NeRF (Huang et al., 2022), the first to synthesize HDR images of novel views using the implicit NeRF model, and (2) HDR-G GS (Cai et al., 2024), which leverages the efficient representation of 3DGS build an HDR representation model. To the best of our knowledge, these the only existing methods specifically designed to synthesize HDR novel views from LDR training imagery. Whilst designed for multi-exposure LDR setting, they can be also applied to our proposed single-exposure setting. We used their official repositories to conduct the experiments for ensuring their own optimal performance.
In addition visual quality assessment, model inference speed (fps) is also included. We highlight the following key points:
(I) In terms of HDR-NVS results, our models significantly outperform all alternatives, particularly HDR-NeRF, in gen- eration quality. This advantage arises because HDR-NeRF struggles to converge without multiple exposure LDR train- ing data, often producing images that are entirely black or white. This highlights the greater challenges associated with the proposed single exposure setting while also vali- dating the efficacy and superiority of our model design in
Tab. 1 presents the quantitative results the synthetic datasets for both LDR and HDR-NVS. Notably, HDR-NVS
6

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High Dynamic Range Novel View Synthesis with Single Exposure
0:125e
325
(b)
0.667s
(c)
HDR-GS
Mono-HDR-GS (Ours)
Ground Truth
Figure 6. Comparison of LDR-NVS on both (a/b) synthctic and (c) real datasets. At: Exposure time.
addressing such challenges.
Table 2. Quantitative results on the real datasets. We report the results averaged across all scenes and exposure times tı, ts. and ts. Note, HDR results cannot be reported due to no ground-truth,
(II) Regarding the LDR-NVS results, we observe a similar performance advantage with our models. This indicates that directly learning HDR model from single-exposure LDR data, competitors do, would be inferior due to the absence of multiple exposure observations. This also partly explains our stronger HDR-NVS results, which somehow are dependent on the quality of the LDR output.
LDR result
Method
PSNR1
SSIM
LPIPS./
HDR-NeRF
32.50
0.948
0.069
Mono- MEDR-NeRF (Ours)
32.52
0.948
0.069
(III) terms of efficiency, our models perform comparably alternatives using either NeRF or 3DGS the represen- tation model. This suggests that models do not sacrifice efficiency for the sake of quality.
HDR-GS
35.34
0.966
0.019
Mono-HDR-GE (Ours)
35.81
0.967
0.017
tures. Regarding LDR-NVS results, as illustrated in Fig. 6. HDR-GS tends to produce blurry and visually unappealing results (e.g., the synthetic data case) or fails in rendering extremely bright and contrastive regions (e.g., the real data case). In contrast, our Mono-HDR-GE can successfully recover smoother color details and present the brightness properly for such challenging cases.
Result analysis on real data. While less important, Tab. 2 presents the quantitative results of LDR-NVS on the real datasets, as there are no ground-trute trtth HDR images available. The results are averaged across three distinct exposure times and encompass all scenes. It evident that no method clearly stands out in synthesis quality.
We make similar observations when comparing NeRF-based models, as reflected in Fig. (Also see Fig. 8 in Appendix A), where HDR-NeRF produces color artifacts and blurry outputs, while Mono-HDR-NV -NRF achieves superior color consistency and detail preservation.
4.2. Qualitative evaluation
Numerical metrics such as PSNR. SSIM, and LPIPS may not fully reflect the perceived quality of images. Therefore, qualitative evaluation through visual comparison is es- sential. For HDR-NVS results on both synthetic and real datasets, as shown in Fig. 5, HDR-GS struggles to accu- rately reconstruct the darkest and brightest details, whereas our Mono-HHDR-e excels in rendering more intricate struc-
4.3. Ablation studies
We conduct an ablation study with the most efficient model, Mono-HDR HD0--DRR-GS GS, on the synthetic datasets.

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High Dynamic Range Novel View Synthesis with Single Exposure
At 0.125s
HDR-NeRF
Monv-IDRR-NeFF (Ours)
Ground Truth
Figure 7. HDR reconstruction comparison on synthetic datasets. Al: Exposure time.
Table Ablation analysis the synthetic datasets. The results are averaged across exposures and scenes. MLP: Alternative design with similar amount of parameters.
Table 5. Ablation analysis of different loss components the synthetic datasets. Results are averaged across exposures and scenes.
HDDR result
Row
12H-CC
H2I-CC
HDR result
Row
Loss
MLP
Ours
MLP
Ours
PSNR1
SSIM
LPIPS.
PSNR1
SSIM
LPIPS
V
19.02
0.778
0.327
1
Lia
38.43
0.974
0.015
2
Lide
33,93
0.925
0.050
38.57
0.975
0.012
3
LazI
11.87
0.504
0.371
4
Lidr Lodr
38.19
0.974
0.015
Table 4. Ablation analysis of closed-loop design on the synthetic datascts. The results are averaged across exposures and scenes.
5
Lis Laai
13.50
0.507
0.359
6
Lidr + Laai
33.58
0.934
0.058
Janr Trate Tazi
38.57
0.975
0.012
HDR result
Row
L2H-CC
H2L-CC
PSNRT
SSIM
LPIPS.
X
38. 38.57
0.974 0.975
0.015
0.012
Loss contributions. Based on our proposed Mono-HDR- GS, conduct systematic ablation studies to evaluate the contribution of each loss component in Eq. (9). The results in Tab. 5 reveal three key observations:
Module design. To evaluate the design of the proposed L2H-CC and H2L-CC modules, we compare them with an alternative MIP with similar number of parameters. We make several observations from Tab. 3:
(I) Row 2 vs. Row 7: HDR loss Lide serves as the founda- tional component for HDR-NVS performance. When used alone, Mono-HDR-CE achieves moderate metrics (33.93 dB PSNR, 0.925 SSIM), while its absence leads to severe degradation (e.g.. Row 5 shows 13.50 dB PSNR without Ludr). This validates its critical role in reconstructing high dynamic range scenes.
(I) Row 1 vs. 3: With a plain MLP to replace L2H-CC, the model performance will degrade significantly, validating the importance our simulating the camera imaging process (see Sec. 3.2).
(II) Row vs. 3: When replacing H2L-CC with MLP, also observe performance drop, although quite slight, suggesting that our closed-loop design is useful even when HDR ground truth is available.
(II) Row vs. Row 6: LDR loss Lute provides essential regularization for scene modeling. When combined with Laar. it improves PSNR by +4.26 dB (Row vs. 4) and maintains structural fidelity. Notably, Lids alone fails to train (Row 1), but acts complementary constraint when paired with HDR-aware objectives.
Efffec of closed-loop design. Following the above design, further look into the effect of our closed-loop design with H2L-CC with HDR training data. The results in Tab. 4 indicate that this design brings positive impact of 0.38 dB increase in PSNR for HDR-NVS, demonstrating that the closed-loop framework contributes significantly to enhanc- ing the quality of the reconstructed HDR images.
(III) Row 4 vs. 7: Closed-loop loss Lαd1 enhances photo- metric consistency across exposure domains. Its integration elevates PSNR by +0.38 dB while reducing perceptual dis- tance. This demonstrates that bidirectional optimization between HDR and LDR spaces refines both radiance esti- mation and tone mapping.
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High Dynamic Range Novel View Synthesis with Single Exposure
5. Conclusion
Table 6. Ablation studies of different ratio of I.DR/ / HDR images.
This paper pioneers the Single-Exposure HDR-NVS prob- lem by introducing Mono-HDR-3D. a novel meta- algorithg designed to operate effectively with only single-exposure LDR images during training. Unlike conventional HDR- NVS approaches that rely on multiple-exposure imagery, Mono-HDR-3D addresses critical limitations such as mo- tion artifacts, high capture and storage costs, and the need for precise exposure tuning. This not only enhances ap- plicability but also simplifies deployment dynamic and rapidly changing scenes. Extensive experimental evalua- tions demonstrate that Mono-HDR-3D significantly outper- forms existing methods in generative quality under such more challenging conditions. Importantly, the seamless integration capability of Mono-HDR-3D with existing 3D representation models highlights its versatility and potential for widespread adoption, making advanced HDR techniques accessible to a broader audience and even future advance- ment in representation modeling.
HDR result
Method
LDR/ / HDR
PSNRT
SSIM
LPIPS|
HDR-GS
35.30
0.965
0.030
1/1
Mono-HDR-GS
38.57
0.975
0.012
HDR-GS
35.26
0.963
0.033
2/1
Mono- HDR-GS
37.97
0.975
0.013
HDR-GS
35.16
0.958
0,035
3/1
Mono-HDR HDR-GS
37.53
0.974
0.014
HDR-GS
34,89
0.961
0.027
5/1
Mono-HDR-Co
35.51
0.963
0.023
HDR-GS
33.46
0.936
0.075
0/1
Mono- Mono-HDR-GS
33.93
0.925
0,050
HDR-GS
10.51
0,503
0,350
1/0
Mono-HDR-GS
13.50
0,507
0.359
This work opens new avenues for efficient and robust HDR scene modeling, especially in contexts where access to ex- pensive, professional cameras for training data collection is limited or not possible. By democratizing the process of HDR imaging, we empower more individuals and orga- nizations even with limited resources to engage with high- quality imaging technologies.
Performance of different LDR/ HDR ratios. To evalu- ate the robustness of our Mono-HDR-GS framework under varying data availability, we conduct experiments with LDR / HHR ratios ranging from 0/1 (pure HDR) to 5/1 (dominant LDR), as shown in Tab. 6. The results demonstrate:
(I) Data Efficiency: Our model maintains strong perfor- mance even with sparse HDR data. For instance, at 5/1 ratio, Mono-HDR-GS retains 92.6% of its peak PSNR (35.51 dB vs. 38.57 dB at 1/1), suggesting effective knowledge transfer from LDR supervision.
A Acknowledgments
This work was supported in part by the Key Research and Development Plan of Jiangsu Province (Industry Fore- sight and Key Core Technology Project) under Grant No. BE2023008-2.
(II) LDR Supervision: When HDR data becomes extremely scarce (5/1 vs. 0/1), the PSNR degradation of Mono-HDR- GS (from 35.51 dB to 33.93 dB) is significantly smaller than HDR-GS (from 34.89 dB to 33.46 dB). This confirms that LDR supervision provides a more robust geometric prior for HDR reconstruction.
Impact Statement
In this work, we address the high demands for high-quality visual generation and immersion as required in many fields such as VR / AR, entertainment, creative media, broadcast- ing, TV, and gaming. This has great potential for democra- tizing both the academic research often featured with limited resources, well as related industries with diverse back- grounds and contexts. Unlike existing methods requiring costly multi-exposure LDR imagery capture, our approach enables HDR scene reconstruction from single-exposure images with strong accuracy and realism.
(III) HDR Criticality: Pure HDR supervision (0/1) out- performs pure LDR supervision (1/0) by +23.4 dB PSNR, validating the irreplaceable role of HDR data in captur- ing radiance information. This finding emphasizes HDR's ' fundamental importance for high-qualit, HDR novel view synthesis.
(IV) Overall Superiority: Across all ratios, Mono-HDR- GS consistently outperforms HDR-GS with statistically sig- nificant margins. At equal computational cost, our Mono- HDR-GS achieves up to +3.27 dB PSNR improvement (1/1 ratio) and maintains superior structural fidelity (SSIM 0.975 vs. 0.965). Notably, even with 100% LDR data (1/0 ra- tio), our method generates marginally better results than HDR-GS trained solely on HDR images, demonstrating the effectiveness of closed-loop design.
While this research problem and our technology are still in the early stage, potential misuse risks (e.g., malicious content generation) might warrant ethical considerations. We advocate for responsible deployment and emphasize that its benefits in advancing safer, high-fidelity digital environ- ments outweigh foreseeable risks per se.
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High Dynamic Range Novel View Synthesis with Single Exposure
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High Dynamic Range Novel View Synthesis with Single Exposure
A. Additional Visualization Comparisons of HDR-NeRF and Mono-HDR-NeRF
This section presents additional visual comparisons between HDR-NeRF and our Mono-HDR-NeRF, including LDR novel view rendering results on both synthetic and real datasets. These comparisons are illustrated in Fig. 8.
25
(a)
di 0.125s
(b)
de 0.05x
(c)
HDR-NoRF
Mono-HDR-NoRF (Ours)
Ground Truth
Figure Comparison of LDR-NVS on both (a/b) synthetic and (c) real datasets. Al: Exposure time.
It can be observed that, HDR-NeRF suffers from color artifacts and blurriness when rendering LDR images, and may fail to converge, producing black outputs without multi-exposure data, In contrast, Mono-HDR-NeRF achieves superior color consistency and detail preservation in LDR rendering.
12


Reviews:
[{"review_id": "yjPUmhTkAd", "reviewer": "Reviewer_dY8v", "summary": "This paper proposes a novel method for HDR scene novel view rendering with single-exposure LDR images. The approach involves two key components: an LDR-to-HDR (L2H) converter and an HDR-to-LDR (H2L) converter, both designed based on the camera imaging process. The L2H module first converts LDR images into HDR representations, while the H2L module generates LDR images for supervision using the input images. Experiments on synthetic datasets demonstrate that this method achieves superior rendering quality compared to existing approaches.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Reviewer dY8v\n**Q1: The proposed L2H-CC converts an LDR model to an HDR model, however, after reading the paper, I'm not completely clear how its design prevents the module from learning a trivial solution of mapping to an LDR instead of HDR mode.**\n\nLet us summarize the key features of our method:\nFirst, previous methods such as HDR-GS (Cai et al., 2024) and HDR-NeRF (Huang et al., 2022) are inferior in design to tackle this more challenging single-exposure HDR-NVS problem since directly learning a HDR scene model from single-exposure multi-view imagery is extremely challenging (see L86-93).\nWith Mono-HDR-3D, we instead first learn a LDR scene model. More importantly, we impose the inherent camera imaging mechanism [1-2] (see the camera imaging formula Eq. (6), Sec 3.2) to facilitate HDR color estimation - enabling more robust translation from LDR to HDR by leveraging imaging physics prior knowledge (L190-216, Sec. 3.2). We will further clarify.\n\n[1] Noise-optimal capture for high dynamic range photography[C]//CVPR, 2010: 553-560.\n[2] Compressed-SDR to HDR Video Reconstruction[J]//TPAMI, 2023, 46(5): 3679-3691.\n\n**Q2: How does Mono-HDR-GS use HDR loss? The paper claims to only use LDR images, there should be no HDR ground truth during optimization.**\n\nApologies for this misunderstanding. As stated in L255-265 that, our model supports both cases, with and without HDR ground truth. Given HDR data, the HDR loss $L_\\text{hdr}$ is used along with the others as shown in Eq. (9).\n\n**Q3: The ablation study of closed-loop design lacks implementation details. How does supervision work without “H2L-CC”? What supervision is used to learn “L2H-CC”?**\n\nIn the closed-loop ablation study (Tab. 4), when the H2L-CC module is omitted, the HDR loss $L_\\text{hdr}$ directly supervises the L2H-CC module to learn the LDR-to-HDR transformation, and $L_\\text{ldr}$ enforces the input to L2H-CC as a valid LDR model. Additionally, if H2L-CC exists, an extra loss $L_\\text{h2l}$ will be added to supervise the L2H-CC.\n\n**Q4: It’s better if authors can add ablation on the losses, $L_\\text{ldr}$, $L_\\text{hdr}$, and $L_\\text{h2l}$ for a more comprehensive evaluation.**\n\nThanks! As suggested, we have now conducted an exhaustive analysis of loss combination. The results of HDR rendering are reported below:\n| Index |      Loss      | PSNR ($\\uparrow$) | SSIM ($\\uparrow$)  | LPIPS ($\\downarrow$) |\n|:--:|:--------------:|:---------:|:-----:|:-----:|\n| 1 |$L_\\text{ldr}$ |  -        |  -    |   -   |\n| 2 |$L_\\text{hdr}$ | 33.93     | 0.925 | 0.050 |\n| 3 |$L_\\text{h2l}$ | 11.87      | 0.504 | 0.371 |\n| 4 |$L_\\text{ldr}$ + $L_\\text{hdr}$ | 38.19 | 0.974 | 0.015 |\n| 5 |$L_\\text{ldr}$ + $L_\\text{h2l}$ | 13.50  | 0.507 | 0.359 |\n| 6 |$L_\\text{hdr}$ + $L_\\text{h2l}$ | 33.58 | 0.934 | 0.058 |\n| 7 |$L_\\text{ldr}$ + $L_\\text{hdr}$ + $L_\\text{h2l}$ | **38.57** | **0.975** | **0.012** |\n\nWe highlight that:\n- HDR loss $L_\\text{hdr}$ is basically important as expected;\n- LDR loss helps clearly by properly supervising the LDR scene model optimization;\n- The closed loop loss $L_\\text{h2l}$ adds further value on top.", "claims_and_evidence": "The major claims are supported by experiments.", "methods_and_evaluation_criteria": "The proposed methods make sense for the problem.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "The experimental designs are valid.", "relation_to_broader_scientific_literature": "The paper is significant in the field of NVS since it proposes a novel method to create HDR representations with single exposure LDF images.", "essential_references_not_discussed": "References are adequately discussed.", "other_strengths_and_weaknesses": "Strength\nThe design of HDR and LDR converters is novel. It’s interesting to use only single-exposure LDR images to generate HDR 3D representation, which can further inspire fields in view synthesis and inverse rendering. Results on synthetic data are promising.\n\nWeakness\n- The proposed L2H-CC converts an LDR model to an HDR model, however, after reading the paper, I'm not completely clear how its design prevents the module from learning a trivial solution of mapping to an LDR instead of HDR mode.\n- Important technical details on implementation and experiments are missing and could benefit from more explanation:\n1. How does Mono-HDR-GS use HDR loss? The paper claims to only use LDR images, there should be no HDR ground truth during optimization. \n2. The ablation study of closed-loop design lacks implementation details. How does supervision work without “H2L-CC’? What supervision is used to learn “L2H-CC’?\n3. It’s better if authors can add ablation on the losses, l_{ldr}, l_{hdr}, and l_{h2l} for a more comprehensive evaluation.\n\nThe paper presents an interesting approach to HDR novel view synthesis using only LDR images, without relying on any data-driven priors. However, my main concerns lie in the lack of clarity in certain technical explanations and the omission of crucial experimental details. Notably, while the paper claims to train solely with LDR images, the implementation in Section 3.3 appears to incorporate HDR image loss, which raises questions about the training setup. A more positive rating will be considered if the authors can thoroughly address these concerns.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "See Other Strengths And Weaknesses.", "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": "jjJjgUobyL", "reviewer": "Reviewer_yk8h", "summary": "This paper introduces Mono-HDR-3D, a novel single-exposure HDR-NVS approach that reconstructs 3D HDR scenes in NeRF or 3DGS using only LDR images, eliminating the need for multi-exposure inputs. The method comprises two modules based on LDR image formation principle, which is LDR-to-HDR module that predicts HDR details from LDR images, and HDR-to-LDR module that allows the model to be trained with LDR images.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Reviewer yk8h\n\n**Q1: I question the necessity of H2L module: what happens if you render the image in HDR, and simply convert it to LDR with existing modules or analytic method, instead of approximating it with MLP? Is this case not possible because there are no module which support backpropagation for training? If not, how does the model perform in the case of such naive HDR-to-LDR conversion method? Please elaborate.**\n\nGreat point! Please note analytical HDR-to-LDR conversion needs the camera CRF which is typically unknown. To address this, we thus learn to approximate. We will further clarify.\n\n**Q2: I question the authors about the case when only LDR images are available for optimizing the scene, which the setting implied in the first paragraph of H2L-CC section at Section 3.2. In this case, how is the L2H module able to learn the mapping from LDR to HDR? It seems that this module is not generalizable and optimized per scene with NeRF or 3DGS, so I suppose it would not be able to receive and guidance signals for learning L2H in cases when no HDR images are available. I ask the authors to provide additional elaboration and experimental results for this setting.**\n\nWithout HDR ground truth, any model will be less properly constrained including ours. However, compared with previous methods, our Mono-HDR-3D has advantages due to leveraging the inherent camera imaging mechasnim (imaging physics prior) along with the closed-loop design (H2L-CC followed by L2H-CC, forming a loop). \n\nAs suggested, we now quantitatively evaluate two no-reference image quality assessment (NR-IQA) metrics: NIQE [3] and CLIP-IQA [4], without the need for HDR ground truth. We report the HDR results on real-world datasets below:\n\n| Method | NIQE ($\\downarrow$) | CLIP-IQA ($\\uparrow$) |\n| :----: | :---:| :------: |\n| HDR-GS | 6.40 | 0.48 |\n| Mono-HDR-GS | **3.63** | **0.50** |\n\nThis test validates the efficacy of our model design.\n\n[3] Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer. IEEE Signal processing letters, 2012.\n[4] Wang J, Chan K C K, Loy C C. Exploring clip for assessing the look and feel of images. AAAI 2023.\n\n**Q3: I believe additional experiment would have had to be performed to validate the performance of this method in cases when different ratio of LDR / HDR images are available, including extreme cases where only LDR or HDR images are available for 3D scene optimization.**\n\nGreat suggestions and many thanks! We have now conducted this suggested experiment, with the ratio of LDR / HDR images being 1/1, 2/1, 3/1, 5/1, 0/1, 1/0, respecitively.The results are reported below:\n\n| Method | LDR / HDR | PSNR ($\\uparrow$) | SSIM ($\\uparrow$)  | LPIPS ($\\downarrow$) |\n|:---:|:-------: | :--:      | :---: | :---: |\n| HDR-GS | 1/1   | 35.30 | 0.965 | 0.030 |\n| Mono-HDR-GS    | 1/1       | **38.57** | **0.975** | **0.012** |\n| HDR-GS         | 2/1       | 35.26 | 0.963 | 0.033 |\n| Mono-HDR-GS    | 2/1       | 37.97 | **0.975** | 0.013 |\n| HDR-GS         | 3/1       | 35.16 | 0.958 | 0.035 |\n| Mono-HDR-GS    | 3/1       | 37.53 | 0.974 | 0.014 |\n| HDR-GS         | 5/1       | 34.89 | 0.961 | 0.027 |\n| Mono-HDR-GS    | 5/1       | 35.51 | 0.963 | 0.023 |\n| HDR-GS         | 0/1       | 33.46 | 0.936 | 0.075 |\n| Mono-HDR-GS    | 0/1       | 33.93 | 0.925 | 0.050 |\n| HDR-GS         | 1/0       | 10.51 | 0.503 | 0.350 |\n| Mono-HDR-GS    | 1/0       | 13.50 | 0.507 | 0.359 |\n\nWe highlight that:\n- As the amount of HDR data decreases, our model degragdes only marginally, suggesting the merit of data efficiency.\n- LDR supervision is useful by providing a better quality scene model to be converted.\n- HDR supervision is most critical as expected.\n- Compared with HDR-GS, overall our model is superior acrss all the cases.\n\n**Q4: I find qualitative results to be somewhat lacking for me to be fully convinced in the performance of this method.**\n\nReconstructing HDR radiance fields from single-exposure LDR inputs constitutes an ill-posed inverse problem, as the limited luminance information fails to satisfy the Nyquist-Shannon sampling theorem for dynamic range recovery. Therefore, the perceptual quality of synthesized HDR images remains fundamentally constrained by the limited luminance information in single-exposure inputs.", "claims_and_evidence": "This paper claims that having a single 3D representation for HDR and approximating the LDR-to-HDR process and HDR-to-LDR process with neural networks brings about performance improvement through its closed-loop design, even in cases where only LDR images are available. In this process, they argue the importance of modeling the architecture in likeness to the camera imaging mechanism. This claim is backed up in the experiment section at Table 3 and 4. \n\nHowever, I question the authors about the case when only LDR images are available for optimizing the scene, which the setting implied in the first paragraph of **H2L-CC** section at Section 3.2. In this case, how is the L2H module able to learn the mapping from LDR to HDR? It seems that this module is not generalizable and optimized per scene with NeRF or 3DGS, so I suppose it would not be able to receive and guidance signals for learning L2H in cases when no HDR images are available. I ask the authors to provide additional elaboration and experimental results for this setting.", "methods_and_evaluation_criteria": "Please view Claims and Evidence: I believe additional experiment would have had to be performed to validate the performance of this method in cases when different ratio of LDR / HDR images are available, including extreme cases where only LDR or HDR images are available for 3D scene optimization.", "theoretical_claims": "See Claims and Evidence.", "experimental_designs_or_analyses": "The soundness and validity of experimental designs and analysis have been verified. However, I find qualitative results to be somewhat lacking for me to be fully convinced in the performance of this method.", "relation_to_broader_scientific_literature": "This paper builds upon and extends several key areas of research in HDR imaging, Novel View Synthesis (NVS), and computational photography by addressing the limitations of existing HDR-NVS methods and introducing a new single-exposure-based approach.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "- This paper is well-written and easy to follow.\n- The architecture that emulates real-life LDR-to-HDR process and HDR-to-LDR process is somewhat novel, though the idea of basing the model architecture in real-life physical properties is familiar and well-known in the field of novel view synthesis.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "I question the necessity of H2L module: what happens if you render the image in HDR, and simply convert it to LDR with existing modules or analytic method, instead of approximating it with MLP? Is this case not possible because there are no module which support backpropagation for training? If not, how does the model perform in the case of such naive HDR-to-LDR conversion method? Please elaborate.", "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": "YTbT6R05GR", "reviewer": "Reviewer_nprx", "summary": "This paper studies the high dynamic range novel view synthesis problem with only single-exposure LDR images given.\n\nThe authors propose a generic framework, Mono-HDR-3D, that learns to capture the underlying camera imaging process for bridging LDR and HDR space effectively under the challenging single exposure scenario. Designed as a generic approach, this method can be integrated with different 3D scene models such as NeRF and 3DGS.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Reviewer nprx\n\n**Q1: How to validate the two MLPs L2H-CC and H2L-CC decompose the camera imaging process. There are no supervision in the loss function to ensure this part.**\n\nGreat question! It is exaclty due to no such supersion that makes the problem extremely challenging. It is the architecture of the two convertors (Fig. 3 & 4) we introduce here that imposes structural prior of camera imaging (Eq. (6)) to drive the model approaximate the underlying camera imaging process. This has been validated in ablation study (see Tab. 3) by comparing with plan MLP without such structure.\n\n**Q2: The improvements on real scenes are marginal. Why? The authors do not explain this.**\n\nOn real scenes without HDR ground truth, the improvement is indeed more challenge to acquire. However, to provide more evidence, we have now quantitatively evaluated two no-reference image quality assessment (NR-IQA) metrics: NIQE [3] and CLIP-IQA [4], without the need for HDR ground truth. We report the HDR results on real-world datasets below:\n\n| Method | NIQE ($\\downarrow$) | CLIP-IQA ($\\uparrow$) |\n| :----: | :---:| :------: |\n| HDR-GS | 6.40 | 0.48 |\n| Mono-HDR-GS | **3.63** | **0.50** |\n\nThis test further indicates the meaningful superiority of our method over previous alternatives. Pleases note, Tab. 2 (main paper) reports the results of LDR rendering which is not the focus of this work.\n\n[3] Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer. IEEE Signal processing letters, 2012.\n[4] Wang J, Chan K C K, Loy C C. Exploring clip for assessing the look and feel of images. AAAI 2023.\n\n**Q3: Code and models are not submitted. The reproducibility cannot be checked.**\n\nOur code and models will be released later.\n\n**Q4: I suggest the authors to re-organize the paper to remove the blank in lines 159-164.**\n\nThanks, we will.\n\n**Q5: The HDR results in Fig. 5\\(c\\) look very terrible and are totally different from those in the original paper of HDR-GS.**\n\nGreat spot, but please note that our single-exposure setting is a more challenging and more practical, as comthe to the conventional multi-exppsure setting as used in the HDR-GS. This contrast illustrates exactly the challenges with our new setting (e.g., the limited luminance information cannot fulfill the Nyquist-Shannon sampling theorem requirements for dynamic range recovery). We used the official codes of HDR-GS to ensure the correction (with the same code, we can reproduce the results of multi-exposure setting).\nPlease note, this work does not fully solve the single-exposure HDR-NVS problem but indicates a meaningful forward step and forster more advanced research in the future.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "This work studies the 3D HDR imaging problem. It is related to 3D reconstruction techniques like NeRF and 3DGS. The most related work is HDR-GS", "essential_references_not_discussed": "The references are fairly enough.", "other_strengths_and_weaknesses": "Strengths:\n\n(i) This work studies a more difficult and novel problem, single-exposure high dynamic range novel view synthesis, which is more challenging as previous methods all require at least three exposure times to learn the mapping function from LDR to HDR in 3D space.\n\n(ii) The idea of decomposing the tone-mapping function into the camera imaging process is interesting. Based on this decomposition, the authors design the low dynamic to high dynamic color converter (L2H-CC) and high dynamic to low dynamic color converter. This is good and insighted.\n\n(iii) The writing is good and clear. Especially the mathematical notations in section 3. The presentation is well-dressed. Especially the workflow paradigm of the pipeline in figure 2.\n\n(iv) The performance is good and solid. As shown in Table 1. The improvements on HDR-NeRF and HDR-GS are 19 dB and 3 dB. Very impressive.\n\n\nWeaknesses:\n\n(i) How to validate the two MLPs L2H-CC and H2L-CC decompose the camera imaging process. There are no supervision in the loss function to ensure this part.\n\n(ii) The HDR results in Figure 5 (c) look very terrible and are totally different from those in the original paper of HDR-GS. \n\n(iii) The improvements on real scenes are marginal. Why? The authors do not explain this.\n\n(iv) Code and models are not submitted. The reproducibility cannot be checked.", "other_comments_or_suggestions": "I suggest the authors to re-organize the paper to remove the blank in Line 159 - 164.", "questions_for_authors": "How did you visualize the HDR results of real scenes? More visualization results should be provided.", "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": "swcqmq8fsy", "reviewer": "Reviewer_ZUxK", "summary": "The paper introduces Mono-HDR-3D, a framework for High Dynamic Range Novel View Synthesis (HDR-NVS) that operates effectively with only single-exposure Low Dynamic Range (LDR) images during training. The approach addresses limitations of previous multi-exposure methods by proposing a meta-algorithm that includes two dedicated modules: an LDR-to-HDR Color Converter (L2H-CC) and an HDR-to-LDR Color Converter (H2L-CC), forming a closed-loop design.  Experimental results on synthetic and real datasets demonstrate significant improvements in HDR novel view synthesis quality compared to previous state-of-the-art methods (HDR-NeRF & HDR-GS).\n\n## update after rebuttal\n\nMany thanks to the rebuttal. Out of concern for the rigor expected from reviewers and the noticeable lack of many relevant related works, I maintain my recommendation to reject.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Reviewer ZUxK\n\n**Q1: While it may be relatively straightforward to outperform HDR-GS and HDR-NeRF when tailoring the design to a specific setting, the real challenge lies in demonstrating that the method can also surpass these baselines under their own conditions.**\n\nGreat point! As suggested, we have now evaluated Mono-HDR-GS under the conventional multi-exposure setting. As shown in the results below, we show that overall, our method achieves superior performance for both HDR and LDR rendering. We will add this test in the final version.\n\nHDR rendering results on the synthetic datasets.\n| Method | PSNR ($\\uparrow$) | SSIM ($\\uparrow$) | LPIPS ($\\downarrow$) |\n| :----: | :----------:  | :------: | :-------: |\n| HDR-GS | 38.31 | 0.972 | 0.013 |\n| Mono-HDR-GS | **38.66** | **0.976** | **0.012** | \n\nLDR rendering results of observed exposure (LDR-OE) on the synthetic datasets.\n\n| Method | PSNR ($\\uparrow$) | SSIM ($\\uparrow$) | LPIPS ($\\downarrow$) |\n| :----: | :----------:  | :------: | :-------: |\n| HDR-GS | **41.10** | 0.982 | **0.011** |\n| Mono-HDR-GS | 40.55 | **0.983** | **0.011** | \n\nLDR rendering results of novel exposure (LDR-NE) on the synthetic datasets.\n| Method | PSNR ($\\uparrow$) | SSIM ($\\uparrow$) | LPIPS ($\\downarrow$)|\n| :----: | :----------:  | :------: | :-------: |\n| HDR-GS | 36.33 | 0.977 | 0.016 |\n| Mono-HDR-GS | **36.43** | **0.979** | **0.014** | \n\n\n**Q2: The authors consider ideal conditions, where multi-view images have the same exposure. However, in real-world scenarios, this is difficult because each camera has different exposure settings.**\n\nApologies for this misunderstanding (multi-view vs. multi-camera). Under the proposed single-exposure setting, even a single camera device with a single shutter time setup would suffice to acquire multi-view training imagery. This is more convenient than the multi-camera requirement as the reviewer mentioned. We will clarify this.\n\n**Q3: Some LDR novel view synthesis methods maybe need to discuss, which handle lightness correction in LDR domain, and may could connect with 2D inverse tone mapping methods to serve as a baseline, i.e. [5-7]. It might be more reasonable to add some 2D inverse tone methods and basic 3DGS combinations to the comparison methods.\n[5] Lighting up NeRF via Unsupervised Decomposition and Enhancement, ICCV 2023; \n[6] Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption, AAAI 2024;\n[7] A Bilevel Optimization Approach for Novel View Synthesis, AAAI 2024.**\n\nThanks for the suggestions. We will discuss them though being less relevant, as they either focus on luminance correction (vs. our color space transformation) or single image cases (vs. our 3D scene modeling). They are thus not proper competitors due to addressing different problems.\n\n**Q4: Currently, the baselines are limited, and the paper lacks more depth analyze about how to ensure multi-view consistency.**\n\nTo the best of our knowledge, HDR-NeRF and HDR-GS are the only two state-of-the-art methods for HDR-NVS. We are more than happy to include more if suggested. The core of our model lies in how to learn the mapping from LDR to HDR, whilst multi-view consistency is ensured by the 3D scene model (e.g., 3DGS or NeRF) adopted. As a result, our approach is open and generic to integrate with any 3D representation models. \n\n**Q5: Would it be possible to include some video comparison results in the supplement? This could make the visual effects more apparent, especially regarding 3D consistency.**\n\nGreat suggestion! We will add video results.", "claims_and_evidence": "The evidence presented is convincing and supports the claims made by the authors. The experiments span both synthetic and real datasets, and the comparisons are conducted in a fair and comprehensive manner. Additionally, the ablation studies effectively isolate the contributions of various components within the proposed framework.\n\nHowever, while it may be relatively straightforward to outperform HDR-GS and HDR-NeRF when tailoring the design to a specific setting, the real challenge lies in demonstrating that the method can also surpass these baselines under their own conditions (i.e., with inconsistent exposure times). Addressing this would provide stronger evidence for the generalizability of the approach.\n\nA major problem is that the authors consider ideal conditions, where multi-view images have the same exposure. However, in real-world scenarios, this is difficult because each camera has different exposure settings (i.e., ISO, exposure time). I believe that single-view reconstruction is more suitable for scenes with a single exposure, rather than multi-view reconstruction. This is my biggest confusion with the paper.", "methods_and_evaluation_criteria": "The proposed methods make sense for the problem of HDR-NVS with single-exposure LDR images. The architecture of Mono-HDR-3D, with its dedicated color conversion modules and closed-loop design, directly addresses the challenge of learning HDR representations from limited information. \n\nThe evaluation criteria (PSNR, SSIM, LPIPS) are standard for novel view synthesis tasks and appropriate for assessing both quantitative quality and perceptual performance, this part is OK for me.", "theoretical_claims": "The paper does not present extensive theoretical proofs but rather focuses on conceptual framework and experimental validation, for me this is not a big problem.", "experimental_designs_or_analyses": "This paper utilizes appropriate benchmark datasets that include both synthetic and real scenes, compares its approach against state-of-the-art methods such as HDR-NeRF and HDR-GS, incorporates both quantitative metrics and qualitative visual comparisons, and conducts ablation studies to validate its design choices.", "relation_to_broader_scientific_literature": "Closely related to HDR-NeRF and HDR-GS, but improve with only need single exposure time. \n\nHowever, I believe the innovation is not sufficiently strong. Revising the paper does not allow for the exploration of a more challenging setting, such as single-view, in addition to single-exposure. In an ideal single-exposure scenario, it often only captures a single view. The assumptions made in this paper are therefore too restrictive.", "essential_references_not_discussed": "Some LDR novel view synthesis methods maybe need to discuss, which handle lightness correction in LDR domain, and may could connect with 2D inverse tone mapping methods to serve as a baseline.\n\nLike : \n[1]. Lighting up NeRF via Unsupervised Decomposition and Enhancement , ICCV 2023\n[2]. Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption, AAAI 2024\n[3]. A Bilevel Optimization Approach for Novel View Synthesis, AAAI 2024", "other_strengths_and_weaknesses": "Would it be possible to include some video comparison results in the supplement? This could make the visual effects more apparent, especially regarding 3D consistency.", "other_comments_or_suggestions": "It might be more reasonable to add some 2D inverse tone methods and basic 3DGS combinations to the comparison methods. \n\nCurrently, the baselines are limited, and the paper lacks more depth analyze about how to ensure multi-view consistency.", "questions_for_authors": "See \"Other Strengths And Weaknesses\" and \"Other Comments Or Suggestions\"", "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:

Meta-review: This paper received divergent reviews: two weak rejects, one accept, and one strong accept. The rebuttal addressed most reviewers' concerns and the two accepting reviewers upgraded their scores. Although some reviewers still have concerns about comparison with some concurrent works, the AC feels that the issues are addressable in the revision. The AC's recommendation is therefore accept.
Decision: Accept (poster)