Learning Neural Exposure Fields for View Synthesis
Authors: Michael Niemeyer
Fabian Manhardt
Marie-Julie Rakotosaona
Michael Oechsle
Christina Tsalicoglou
Keisuke Tateno
Jonathan T. Barron
Federico Tombari
Venue: NeurIPS 2025 poster ()
Keywords: neural radiance fields
view synthesis
exposure
Primary area: applications

Abstract: Recent advances in neural scene representations have led to unprecedented quality in 3D reconstruction and view synthesis. Despite achieving high-quality results for common benchmarks with curated data, outputs often degrade for data that contain per image variations such as strong exposure changes, present, e.g., in most scenes with indoor and outdoor areas or rooms with windows. In this paper, we introduce Neural Exposure Fields (NExF), a novel technique for robustly reconstructing 3D scenes with high quality and 3D-consistent appearance from challenging real-world captures. In the core, we propose to learn a neural field predicting an optimal exposure value per 3D point, enabling us to optimize exposure along with the neural scene representation. While capture devices such as cameras select optimal exposure per image/pixel, we generalize this concept and perform optimization in 3D instead. This enables accurate view synthesis in high dynamic range scenarios, bypassing the need of post-processing steps or multi-exposure captures. Our contributions include a novel neural representation for exposure prediction, a system for joint optimization of the scene representation and the exposure field via a novel neural conditioning mechanism, and demonstrated superior performance on challenging real-world data. We find that our approach trains faster than prior works and produces state-of-the-art results on several benchmarks improving by over 55% over best-performing baselines.

Learning Neural Exposure Fields for View Synthesis
Michael Niemeyer' Christina Tsalicoglou
Fabian Manhardt Keisuke Tateno
Marie-Julie Rakotosaona
Michael Oechsle Federico Tombari
Jonathan T. Barron Google
Abstract
Recent advances in neural scene representations have led to unprecedented quality in 3D reconstruction and view synthesis. Despite achieving high-quality results for common benchmarks with curated data, outputs often degrade for data that contain per image variations such as strong exposure changes, present, e.g., most scenes with indoor and outdoor areas rooms with windows. In this paper, we introduce Neural Exposure Fields (NExF), a novel technique for robustly reconstructing 3D scenes with high quality and 3DD-conistten appearance from challenging real- world captures. the core, we propose to learn neural field predicting an optimal exposure value per 3D point, enabling us to optimize exposure along with the neural scene representation. While capture devices such as cameras select optimal exposure per image/pixel, we generalize this concept and perform optimization in 3D instead. This enables accurate view synthesis high dynamic range scenarios, bypassing the need of post-processing steps multi-exposure captures. Our contributions include a novel neural representation for exposure prediction, a system for joint optimization of the scene representation and the exposure field via a novel neural conditioning mechanism, and demonstrated superior performance challenging real-world data. We find that approach trains faster than prior works and produces state-of-the results on several benchmarks improving by over 55% over pest-peerorming basclines.
Introduction
Neural scene representations 8
33 50 have revolutionized 3D vision due to their simple design, stable optimization, and offic---f-teetttt performance. As result, they have become the predominant representation for many 3D vision tasks ranging from 3D and 4D reconstruction
19 30 34
53], to generative modeling 20 29
46
view synthesis 3
26 28
While these methods have achieved unprecedented performance on existing view synthesis datasets, the majority of these benchmarks 3 25 factor out several conditions that might occur in real world captures leading to performance gap on such data. Among different phenomena, particular strong exposure and appearance changes can lead to drastically degraded results (see Fig. Tab.
Prior works that consider exposure information during the optimization have explored this from computer graphics perspective and usually aim to either reproduce images with certain exposures or recover high dynamic range (HDR) representations that can be tonemapped via professional software [6 1 15 47]. However, the goal of many applications is not only to reproduce input views the same exposure, but to reconstruct scenes from RGB images in 3D consistent manner and with an appearance that is most faithful to the real world. Another line of work does not consider input exposure and aim to recover 3D consistent scene by explaining away per-image variations. For example, what has been widely adopted in practice is to use generative latent optimization (GLO) embeddings [5) e.g., as done in the pioneering work NeRF-W 21] While being robust solution
"Corresponding author: mieneeyer@ooggle.comm
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Inconsistencies from 2D Tonemapping.
(a) Ignore Exp.
(b) GL.O.
(c) Ours.
Figure Neural Exposure Fields. While state-of-the- art neural fields [ produce high-quality results on clean, well-curated datasets, the quality drops significantly for real-world captures if the exposure variation is ignored (Ta).
(b) 3D Consistent Appearance (Ours),
Figure 2: 3D Consistent Appearance. Prior works 6 15] use 2D tonemap- ping which can lead to inconsistent ap- pearance of the same 3D point even in close-by views (2a). In contrast, our method produces 3D consistent appear- ance across the entire scene (2b).
When equipped with per-view GLO embeddings [4] (21), the results improve (Tbp but scene parts might be over- or underexposed. In contrast, our neural exposure field leads to high-quality 3D consistent scene appearance (Ic while no manual post-processing or reference view is required.
for smaller appearance changes, we find that it leads non-ideal color predictions when drastic variations are present, and parts of the scene can be over- or underexposed (see Fig. [b]. Recently, Bilarf [49] aims learn more local bilateral mappings to explain per-image appearance changes. However separate processing step is required together with a target appearance, e.g., given by an HDR image produced by an artist with professional software.
In this work, we present Neural Exposure Fields (NexF), a novel method to reconstruct scenes with high-quality appearance that shows well-exposed colors while being consistent in 3D (Fig. Tcp. Our key insight is that we can learn neural field that predicts an optimal exposure value per 3D point jointly with the done in capturing devices and previous works, our model is 3D consistent by design, freeing us from separate per-image 2D tonemapping process (see Fig. D Further, we propose to learn neural exposure fields jointly with grid-based radiance fields using a novel latent exposure conditioning mechanism, leading to improved performance. In summary, our contributions are:
representation. By aggregating information in 3D, opposed to in 2D as
A novel neural representation to predict optimal exposure values per 3D point.
A system to jointly learn the exposure field and the neural 3D scene representation with novel latent conditioning mechanism that produces high-quality view synthesis while being consistent in 3D.
A thorough evaluation of the proposed system and baselines where we find that our approach improves over the state-of-the-art by more than 55% MSF
We believe that our proposed system is a significant step towards bringing neural 3D scene represen- tations closer challenging real-world use cases while pushing the quality of view synthesis.
2
Related Work
Neural Fields. Since their emergence in 3D reconstruction g 24 33
50) neural fields have become
leading technique for various 3D vision tasks, e.g., 3D/4D reconstruction [19
30
34
36
53].
3D generative modeling
26 Their widespread adoption can be attributed to factors such simplicity, strong performance, and stable optimiza- tion (50). Unlike more traditional representations such as point clouds
20
38
46), and view synthesis 3 4
37
41, voxels (9),
meshes 12 48) neural fields, especially when parameterized MLP, typically do not require

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complex regularization, manually tuned initializations, specialized optimization mechanisms. As result, we chose represent our exposure field as a neural field that optimized end-to-end along with the neural scene representation.
Neural Fields for View Synthesis. In the context of view synthesis, Neural Radiance Fields (NeRF) 26 have produced unprecedented results by optimizing neural fields using volume rendering which demonstrated greater robustness compared to previous surface-based rendering methods [31 This breakthrough inspired series of follow-up works that improved, among others, the
45 12.
53]. Due to state-of-the-art performance, as our scene representation. Note that we do not choose 3DGS [18) as scene representation in this work due to the degradation on challenging data and less stable joint optimization together with our neural exposure field parameterized by an MLP 32]
rendering quality 2 and speed I 18 28 32
we use a modified version of ZipNeRF
Neural View Synthesis Beyond RGB Captures. Several works have investigated how neural fields can be used for view synthesis from non non-traditional captures. For example, [27 47 investigate raw
captures and 2 [54] low-light captures. Closer related us, 66 13
16 investigate how 15
tonemapping of HDR representations can be learned. HDR NeRF [15] proposes to condition a NeRF model on exposure by transforming the radiance to the log domain, achieving impressive results for HDR data. Nevertheless, HDRNeRF requires commercial 2D tonemapping software [1] and is also not 3D consistent. In [66.. this model is extended with a field to predict local exposure. However, they require two training stages leading to excessive training times and rely on pseudo ground truth generated by the trained NeRF model to supervise the local exposure module, hence being restricted to synthetic, small-scaa scale scenes. In contrast, our method is trained end-to-end by jointly optimizing the NeRF model and the neural exposure field. Further, our model produces high-quality. 3D consistent results even for challenging and large-scale captures with varying exposure.
Neural View Synthesis for In-the- Wild Data. While first view synthesis works were evaluated mostly synthetic very clean real-world captures, latter works shifted focus towards more in-the wild data. The pioneering work NeRF-W W 21 proposes to use Generative Latent Optimization (GLO) embeddings [5] factor out per-image appearance variations. At time, novel views are rendered with fixed embedding (usually the zero vector) to generate a 3D consistent appearance. In [4], this approach is improved by optimizing an affine GLO transformation applied the bottleneck of the MLP. While producing 3D consistent results, there is no direct control over the final predicted color and in the presence of strong exposure changes, approaches a mean prediction which can produce over and underexposure for parts of the scene (see Fig. [b). In Bilarf [49], per-view 3D bilateral grids are optimized and test time, the appearance of target image can be lifted to 3D in a second optimization stage. While producing good results, this is memory-heave requires a reference image that needs to be created by a professional artist or commercial HDR software []]. In this work, we optimize a neural exposure field along with the scene representation that by design produces 3D consistent and high-quality, well-exposed colors without the need of an additional postprocessing step professionally reference image.
time-consuming. and further
3 Method
We first describe our scene representation and latent exposure conditioning in Sec. 3.1 Next, we
explain our neural exposure field along with the weighting criteria in Sec. (3.2] Finally, Sec. 3.3 we outline the optimization strategy and implementation details. In Fig. 3 we show an overview over our method.
3.1 Neural Radiance Fields
A radiance field f maps point in 3D space R&3 together with viewing direction d S to volume density a E +R+ and an RGB color value e E RR3 The final color prediction for a pixel is approximated via quadrature using N, sample points along the ray 17
N
1-1
Cpixel
Σ j=1
Tajyy
T= II (1-aa ax), a, =5=1- 5, = ||x]+1 - x,112
1=1
"Our model can directly be combined with distillation approaches such as 32
3

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just baleey) poot Adll AsPe.())
(x, d)
Test Time
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Denelly Wegglts
Noural Radianer Food
Desilty
fo
Sua
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Input Views with
12, At
Traiu Time
Volume
Loss Calenlation
I1, A41
Varying Exposure
Rendering
Figure 3: Method Overview. Our method takes as input set of RGB images {Tz) with exposure times (iti); and outputs a neural representation that produces high-quality, well-exposed appearance in 3D consistent manner from arbitrary viewpoints. More specifically, during training, points are sampled along the ray and for each point viewing direction d, and input exposure At, the neural field fo predicts a density a and a color c. The final color prediction Cpixel obtained via volume rendering and the model is trained with the MSE loss the input views with varying exposure. Similarly, the neural exposure field is trained by volume rendering the 3D predictions Át to the image plane and backpropagating the reconstruction loss wrt. the input exposure weighted by how well the pixel is exposed and saturated. At test time, the neural field fo is instead conditioned on the neural exposure field predictions Ař, producing high-quality, well-exposed novel views that are consistent in 3D where no 2D tonemapping nor target appearance produced by professional is required.
where Ty is transmittance, aş is the alpha value for X,, and 6g the distance between neighboring samples.
Parameterization. A neural radiance field 26 optimizes an MLP fo parameterized by network weights 0 using gradient descent with reconstruction loss:
Σ (1)) (r)
FE Reateh
where r E Rbatch are sampled batches rays. Using multisampling and a multi-resolution back- bone (28), Zip-NeRF [4] produces state-of-the-art performance in view synthesis and we therefore adopt this architecture in our work. For complex real-world scenes like (51), we add affine GLO embedding vectors as proposed in 4 to increase robustness. Furthermore, we find that for forward- facing scenes, as present in the HDRNeRF [13) dataset, modifications for view deeedeennd color predictions are required prevent overfitting. More specifically, for such scenes, adjust the view-dependent branch from three layers with 256 hidden units to only two layers with 64 hidden units. We also reduce the bottleneck dimensionality 15 and remove the skip connection to the second layer.
Latent Exposure Conditioning, Many real-world captures contain significant exposure variations and provide per-image ground-truth exposure value. This lets us use per-image exposure as input to our model alongside the RGB images, and condition the color prediction on exposure. We follow the classical nonparametric Conditional Random Fields (CRF) calibration [10 [5] and perform the conditioning in the logarithm radiance domain. Unlike prior works, however, we apply the transformation to the bottleneck vector of the MLP:
fe(x, d, A((r)) = f rriew (J (x) In A)(),
(2)
where x is the sample point, d the viewing direction, At(r) the exposure of corresponding ray and 1B*(x the bottleneck vector. Crucially, we assume that 1 (x) predicts log radiance so that we do not require any transformation (see Fig. 5). We find that this latent conditioning leads to improved performance compared to prior works [IS]. During training, we condition our NeRF Se on the input exposure to correctly reconstruct the input images following ( However, at test time condition on the neural exposure field prediction, which we will discuss in the following section.
3.2 Neural Exposure Fields
We propose to optimize a per-scene neural exposure field to predict the optimal exposure for each 3D point:
Co
R
R, x Af(x)
(3)

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A1
Radianoe
Exposure Conditioning 15
A1
MLP
Botslenock Lateut Voctor
MLP
RGB
(b) Latent Exposure Conditioning (Ours).
Figure 5: Exposure Conditioning, While prior works [IS] condition the color pre- diction on the log exposure by adding onto the radiance (5a). we also perform the log transformation but condition in latent space instead (5b), leading to improved performance and more stable optimization.
(a) RGB Prediction.
Exposure Prediction.
Figure 4: Exposure Visualization. Instead of single exposure per image as commonly done, optimize a 3D neural exposure field predicting optimal exposure in 3D leading to well-exposed colors for all parts of the scene (see e.g. short exposure (dark) for outdoor and longer exposure (white) for darker indoor parts above).
where & indicates the network parameters and A((x) the predicted exposure at 3D point x. It is important note that, in contrast to the conventional formulation of exposure, not as a function of the camera location and viewing direction, but as a function of 3D position. This is based on the hypothesis that there exists an optimal exposure value for each 3D point for which the predicted color is well-exposed (see Fig, 4). This lets us learn high-quality appearances for high-dynamic range scenarios that are consistent 3D, in contrast prior works (6] 15 that perform tonemapping on the 2D image plane leading to inconsistent synthesis across views.
define exposure
Parameterization. We parameterize our neural exposure field e as a fully-conmected MLP. We optimize this neural exposure field alongside scene representation fo. As we aggregate exposure information in 3D, in contrast to prior works and capturing devices that operate in 2D, we can define criteria obtain the best-possible "ideal" exposure per point which we will do in the following.
Objective 1: Well-Exposedness. A pixel's color is often referred to as or underexposed if the color value close or at the clipping boundaries. More specifically, we measure well-exposedness as
(G ----///) 1/2)
Wexpp (c) =
II exp
Texp
where Txxp is hyperparameter controlling the sharpness of the weight curve.
Objective 2: Saturation. While our main focus lies on learning ideal exposure per 3D point, we draw inspiration from classical image processing [23] and further integrate objective that aims obtain well saturated colors. More specifically, we define
E( (cs - m.)2
Wsar = 13
(5)
where N. defines the mean of the R, G, and B value of
Regularization. Given that there is no target ground truth 3D exposure available, we find that adding regularization in 3D space leads better results. Intuitively, we hypothesize that the ideal exposure changes smoothly in 3D space as close-by 3D points are presumably well-exposed at similar exposure times. More specifically, we calculate the squared distance between predicted exposure of nearby 3D points
At = |1e cx 1)1
66)
where (0, 0.05) small normally-distribu noise vector. outlined next, we penalize Ataig to regularize our neural exposure field that in turn leads to improved performance.

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3.3 Optimization
We do not assume to have target RGB or HDR appearance but rather learn optimal exposure from 2D observations to produce high-quality, 3D consistent appearance. Our key idea is to selectively backpropagate the exposure of input views only if the color is well exposed and well saturated, according to our objective functions. This way, we do not need to assume that every pixel is well exposed certain views which often not the case, but rather only need to make the assumption that most areas in the 3D scene show well exposed colors in some view. More specifically, we observe that render predicted exposure similarly color Af دド= Tj where a, and 8 are the same in 3.I.. Next, define per-pixel weight
(7) where Aeep and Asas are hyperparameters controlling the influence of the respective mask. For regularization, we can similarly render the exposure difference of nearby sample points Atreg = Σn Tja At dir- We train our neural exposure field eg with the input exposure
w(c) Wexpp (c) -cAP Wsat (C)
(Ø)
Σ (w(c(r)) PERbarchh
A pixe! (r) AA(+)| Afreg())
Joint Optimization. We train our neural scene representation fe and exposure field eq end-to-end from the input captures and the full loss formulation becomes
((), (0) (o)
is important to note that we only require RGB images with exposure as input and no additional labels are required. Using color- criteria, produce high-quality 3D consistent view synthesis results in high dynamic range scenarios while prior works require, e.g., a target HDR image from an artist or access professional HDR software.
Implementation Details. We parameterize our neural exposure field as fully-connected MLP with ReLU activation and four hidden layers with a hidden dimension of 128. For rendering the predicted exposure to the image plane, re-use the alpha blending weights calculated for the RGB rendering pass (see (3.I) for faster performance and detach the gradients so that the scene geometry predic- tion is not affected by the exposure field prediction branch. the NeRF model,
bottleneck vector di- mension 256 per default except for forward-facine scenes where a dimension of 15. We train both models jointly for 10, 000 iterations approx. 10 min.) on forward-facing and for 25, 000 iterations (approx. 30 min.) on room- and apartment-sizze scenes on V100 GPUs. For addi- tional details on the NeRF parameter- ization, we refer the reader 4 We set the weighting-relater hyperparam- eters as Exx 0.05, Xexp 0.1, and Asat =
(a) Clipped HDR.
(b) Single Exposure. (c) Exposure Fusion.
(d) Varying Exposure Values.
Figure Exposure Fusion. During training, each input image is observed with only single randomly-sampe ex- posure (6d). For evaluation, we apply exposure fusion (6a) to obtain higher-quality and well-exposed target images (6c)
4 Experiments
Data. To evaluate our neural scene representation with the latent ex- posure conditioning, we report re- construction results on the HDRN-
compared to the default single exposure images (6b).
cRF [5] dataset (MIT), To further evaluate our model real-world and scene-level captures,

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we also report results on the recently proposed Eyeful Tower dataset v2 [T BY-NNN 4.0). In contrast to other common view synthesis benchmarks like [3, the Eyeful Tower dataset contains HDR images that we can use to simulate varying exposure and to produce meaningful target images. Finally, we test our model on in-the-wild phone captures from 49 (Apache 2.0) and large-scale scene from 4 (CC-BY).
Metries. For the exposure reconstruction experiments, we follow prior works 6 15 and report view synthesis metries PSNR, SSIM, and LPIPS against the ground truth images for both in-distribution and nut-of-distribution exposure values. For evaluating our neural exposure field on room-level scenes, we use the HDR data from the Eyeful Tower [51] dataset that allows us to generate tonemapped images with different exposures. For the training set, for each view we sample an exposure value from an exposure set (1) 8 1 2 2) so that every pose only observed once with one exposure. For evaluation, we use the HDR data to generate each test view with all exposures from our exposure set and then produce the target image by applying classical exposure fusion [23] as illustrated in Fig.
[ To measure how well our method and baselines produce well-exposed reconstructions, we report PSNR, SSIM, LPIPS against these target images on the test set.
Baselines. For the exposure reconstruction experiments, compare against state-of-the-art methods NeRF 26] ZipNeRF 4.. 3DGS [18. NeRF-W [21]. HDRNeRF [13] and HDR-GS 6.. where HDR-GS is the only method that further requires the HDR captures as imput, while all other methods only use RGB at sampled exposure. For the results on the Eyeful Tower scenes, we report our method and all baselines with the same ZipNeRF 4 backbone to enable fair comparison. For these experiments, we compare against ZipNeRF a.) without any modification to handle varying exposure ("Ignore Exposure") ), b.) with GLO embeddings [S] 21), c.) with affine GLO embeddings [4) d.) HDRNeRF* for which we train the NeRF model with exposure input and at test time, we render with constant mean exposure over all input images.
4.1 View Synthesis with Input Exposure
In the first experiment, we evaluate the performance of our chosen neural scene representation and the novel latent exposure conditioning mechanism. The task is to reconstruct test views at different input exposures.
Table Comparison on HDRNeRF. We find that for both in distriibutiion (IN) and cuttof-ddisributtinn (OOD) exposure values, our method overall performs best while training significantly faster than prior works. *Note that HDR-GS G uses the HDR data during training, while all other methods only
rely on RGB images at sampled exposure.
Time
Exposure (1,, l3 4z)
OOD
Exposure (l2, (4)
min.
PSNR f
SSIM
LPIPS
PSNR t
SSIM
LPIPS
NeRF 26
405
13.97
0.555
0.376
14.51
0.522
0.428
ZipNeRF
4
11
19.00
0.682
0.142
19.73
0.724
0.125
3DGS 18
38
19.46
0.690
0.276
18.97
0.778
0.309
NeRF-W 21
437
29.83
0.936
0.047
29.22
0.927
0.050
HDRNeRF 15
542
39.07
0.973
0.026
37.53
0.966
0.024
HDR-GS*
6
34
41.10
0,982
0.011
36.33
0.977
0.016
Ours
11
42.54
0.988
0,014
38.36
0.984
0.021
Quantitative Results. In Tab. T we observe that our method overall leads to the best performance while training 3x faster than the fastest baselines. Compared HDRNeRF [15] the prior state-of- the-art that also uses only RGB sampled at exposure values during training. we improve by 3.5 PSNR from 39.07 42.54 on the in-distribution (ID) exposure values and by 0.8 PSNR from 37.53 to 38. 36 on the out-of-distributin sampled RGB but the full HDR capture during optimization, we also improve PSNR (by 1.4 on ID and 2.0 on OOD exposure) and SSIM while achieving slightly higher LPIPS value. It is crucial to note that RGB input sampled at different exposures is a common scenario for real-world captures, while HDR captures require a professional setup and usually are only available for professionally captured or synthetic environments. We conclude that our method performs best for view synthesis with input exposure compared to SOTA baselines while training at least 3 3x faster.
(OOD) exposure values. Compared to HDR- GS O that requires not only
7

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Table Ablation on HDRNeRE. We ablate our proposed view-dependent MLP architecture for forward- scenes (w/o Our View. MLP) and the latent exposure conditioning (w/o Lat. Con.). Our full model leads to the best results on all metrics.
ID Exposure (/1, 13. (s)
OOD
Exposure (12, 14)
PSNR
SSIM f
I.IPS 4
PSNR 1
SSIM t LPPIS 4
w/o Our View. MLP
33.85
0.928
0.104
28.20
0.880
0.170
w/o Lat. Con. Ours
39.88
0.979
0.038
38.33
0.978
0.034
42.54
0.988
0.014
38.36
0.984
0.021
(a) GT.
(b) Ours.
(c) HDRNeRFs.
(d) GLO.
(e) Affine GLO. (I) Ignore Exp.
Figure 7: Qualitative Results on Eyeful Tower. Results from our model and baselines for the
office_vies and riverview scenes.
Ablation. We additionally perform an ablation study in Tab. 2 for the exposure reconstruction experiment verify the relevance of important components. We find that our model without our view-dependent MLP parameterization (see Sec.3.I] leads to degraded results and tends to overfit to input images. If we do not use our latent exposure conditioning in our model, we find that the quality drops as the model is less expressive and predicted views are less sharp. Our full model leads to the best results on all metrics and we conclude that both contributions are required for high performance.
4.2 View Synthesis with Neural Exposure Field
Table 3: Comparison on Eyeful Tower. Also quantitatively, we find that method performs best compared to state-of-thh-art baselines that all share the same ZipN-
Table 4: Ablation on Eyeful Tower. We ablate our two criteria for the exposure field optimization (w/o Well- Selll-Exoseddeess and w/o Saturation), our regular- ization strategy (w/o Regularization). and the affine
eRF backbone.
GI.O embeddings (w/o A-GLO).
PSNR
SSIM
LPIPS 1
PSNR
SSIM
L.IPS
gnore Exposure
16.72
0.682
0.444
w/o Well-Exposedness
25.92
0.868
0.247
Affine GLO 4
20.13
0.815
0.263
w/o Saturation
24.30
0.867
0.241
GLO
21.20
0.298
w/o Regularization
24.84
0.856
0.258
HDRNeRF
15
22.82
0.836
0.311
w/o A-GLO
27
0.854
0,306
Ours
26.48
0.876
0.234
Ours
26.48
0.876
0,234

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Next, we evaluate how well our method performs at view synthesis with 3D consistent appearance on room- and apartment- scenes with our neural exposure field. In contrast before, the task not to reproduce the appearance at an input exposure, but rather to learn consistent and well-exposed appearance across the entire scene.
Quantitative Results. We find that our method performs best compared baselines in all metrics (see Tab. 3). In PSNR and SSIM, HDRNeRF= [5]] performs second best and we improve over significantly by 57% MSE (+3.77 PSNR, +0.04 SSIM). In LPIPS, affine GLO performs second best and we improve over it by 12% (-0.03 LPIPS).
Qualitative Results. Qualitatively, we observe a similar trend (see Fig. 7 In particular, ignoring exposure leads to floating artifacts and severe scene degradation. Further, all remaining baselines exhibit over- and underexposure in various parts of the scene and lose fine details. In contrast, our method produces well-exposed colors for the entire scene including fine details and far away regions.
Ablation. In Tab. 4 we ablate the main components of our method. We find that both criteria, well-exposedness as well saturation, are required to achieve high-quality re- sults. Without the well-exposedness criterion, especially the LPIPS metric drops, proving that well-exposed colors are perceptually very important. On the other hand, without the saturation criterion, the PSNR metric drops, demonstrating its relevance for faithfully recon- structing well-fused colors (GT). If we remove our proposed regularization, the neural expo- sure field predictions do not generalize as well to novel views, causing a performance drop. Finally, without the affine GLO embeddings, we find that while PSNR slightly increases, both SSIM and LPIPS worsen significantly due to overall more blurry results. Ultimately, con- clude that all components of our method are crucial to achieve the best performance.
(a) GT Test Images.
In-the-Wiin Captures. Finally in Fig. 8 we
additionally show qualitative results for the in- the-wild phone capture statue from [4). We observe that while the input (both train and test) images exhibit strong exposure changes, our method is able to produce a 3D consistent expo- sure that in turn leads to visually appealing, 3D consistent view synthesis. Crucially, compared to prior works, posure as input at test time nor a reference target appearance created by artist with professional software.
(b) Our Exposure Field Predictions.
method does not require
(c) Our RGB Predictions with 3D Consistent Exposure.
Large-Scale Reconstruction. Fig. and T0 we additionally report qualitative results on Alameda, house-sized scene with varying in- put exposure, from the ZipNeRF dataset [4] For all methods, we use the same ZipNeRF 4 back- bone. We find that ignoring the input exposure leads to degenerate view synthesis. Optimizing affine GLO embeddings 4 21 improves the
Figure 8: fn-the-Wildd Captures. For in-the-will
captures [49), both training and test images exhibit strong exposure changes (8ap. Our method learns a 3D consistent exposure field (8b) resulting in 3D consistent high-quality view synthesis (8c).
overall quality but the renderings can still suffer from over- and underexposure. Our method, in con- trast, produces 3D consistent and well-exposed colors for all parts of the scene, while not requiring complex multi-exposure capture as input. is wort noting that in Fig. [n]add our method improves over the ground truth view that captured with a single exposure value.
3D Exposure. While in the traditional sense, exposure is camera-dependent property, our method's s "optimal exposure" a conceptual departure acting as spatially-varying variable. It is not directly a physical property but rather learned representation that guides our model to produce high-quality, well-exposed images, similar to how a photographer might locally adjust exposure high dynamic range photography. By learning 3D exposure field, our model finds per-point exposure value that prevents clipping and under or over-saturation for each point.

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(a) Ignore Exposure.
Affine GL.O.
(c) Ours.
(d) Ground Truth.
Figure 9: Large-Scale View Synthesis. While ignoring the input exposure (9a) leads to degenerate view synthesis, optimizing affine GLO embeddings 21 (b) improves results but output images still suffer from over- and underexposure. In contrast, our method (9c) achieves 3D consistent and well-exposed color for the entire large-scale scene without requiring complex multi-exposure capture, and even improves over the GT test view that was captured with single exposure (9d).
BRa
(a) GT Test View.
(b) Our RGB Prediction.
(c) Our Exposure Field Prediction.
Figure 10: High-Dynamic Range. While input train and test views might show over- or underexpo- sure (00a) complex multi-exposure capture was performed, our model achieves well-exposed colors (10b) for the entire scene thanks to the predictions of the 3D neural exposure field (10c).
Limitations. While our method produces state-of-the-art view synthesis for scenes with varying exposure, results might degrade for extreme lighting conditions such as very low-light or extreme over-exposed captures as well as for complex lighting effects such as semi-transparency and strong reflections.
Conclusion
We presented Neural Exposure Fields (NExF), novel method for view synthesis that produces high-quality 3D-consistent appearances for high dynamic range scenarios. The key idea is to learn 3D neural exposure field from 2D observations by considering a pixel's well-exposedness and saturation for backpropagation, and to train it jointly with our neural scene representation with contrast to previous works, our method does not require
novel latent exposure conditioning. HDR captures nor target appearances produced by professional artists and costly software. We find that our method produces high-quality scene reconstructions with well-exposed colors for parts of the scene, even in challenging high dynamic range scenarios such as indoor rooms with large window fronts overlooking outdoor areas. Also quantitatively, we find that our approach leads to oftt-of-the-rtt performance improving by more than 55% over the best-performing bascline.
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Acknowledgments. We would like to thank Peter Zhizhin, Peter Hedman, and Daniel Duckworth for fruitful discussions and advice, and Cengiz Oztireli for reviewing the draft.
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16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [NA].
Justification: Our method and paper creation did not involve any LLM usage.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy https://eeuri.ccccccomceeeeeee// for what should or should not be described.
20


Reviews:
[{"review_id": "tf4pJUx1c2", "reviewer": "Reviewer_SEiX", "summary": "This paper introduces Neural Exposure Fields (NExF), a method for view synthesis that learns optimal exposure values directly in 3D space to handle scenes with strong lighting variations. Instead of relying on per-image exposure or 2D tonemapping, NExF predicts exposure per 3D point and integrates it into the training of a neural radiance field using a novel latent exposure conditioning mechanism. The method emphasizes well-exposedness and saturation in its loss design and uses regularization to enforce spatial smoothness, leading to high-quality, consistent reconstructions. NExF outperforms prior work in both accuracy and efficiency, achieving over 55% improvement in MSE while requiring less training time and no HDR supervision. It demonstrates strong performance even on complex real-world and in-the-wild datasets, producing photorealistic results without manual tuning or reference views.", "questions": "It would be valuable to evaluate the method on temporally ordered sequences and discuss whether temporal smoothness constraints or recurrent modeling might improve consistency in dynamic or video-based applications.\n\nThe training process assumes access to per-image exposure values. How sensitive is the method to noise or inaccuracy in this metadata?\n\nIt may be helpful to explore adaptive or learned weighting schemes, or to analyze performance under extreme lighting variations to evaluate how generalizable the method is beyond the studied datasets.", "limitations": "The limitations are briefly discussed.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: The paper introduces a novel idea of learning exposure values directly in 3D space, rather than per image or per pixel. This leads to exposure-aware radiance fields that produce consistent and well-exposed renderings even in scenes with high dynamic range, such as indoor-outdoor transitions. The method outperforms existing baselines in both accuracy and training efficiency, achieving clear MSE improvement while requiring no HDR images or manual reference views.\n\n\nWeaknesses:\n1. The exposure field is learned independently of time or sequence, so for video or sequential input, it’s unclear whether predictions would be temporally stable.\n2. The training process assumes access to accurate per-image exposure metadata, which may not always be available.\n3. The well-exposedness and saturation weights are based on fixed formulas and hyperparameters. Although empirically effective. How about the sensitivity of the method to hand-crafted terms and the generalization in drastically different lighting distributions?", "was_revised": true, "final_justification": "Thanks for your explanation and clarification in rebuttal, which resolves most of my concerns. \nI keep my original rating of borderline accept.", "rebuttal": "Thank you for your time and careful review of our work. We find it encouraging that you describe our work as \"introducing a novel idea\" that \"outperforms baselines in both accuracy and training efficiency\". In the following, we address your concerns:\n\n> Weakness: The exposure field is learned independently of time or sequence, so for video or sequential input, it’s unclear whether predictions would be temporally stable. \n\nIt is correct that we address the standard view synthesis task where the input capture is not assumed to be sequential. It is important to note that this is the more general assumption, hence our method can also be used for sequential input. Thanks to our 3D representation of both 3D geometry/appearance and exposure, we find that rendered videos showing fly throughs of our reconstructed scenes appear smooth and stable (please see the videos attached in the supp. mat.). Further, in Figure 2 of the main paper, we show a qualitative comparison of our method with traditional 2D tonemapping of two close-by frames. While our method produces stable colors for the same 3D point, the 2D tonemapping operation leads to significant color changes for the same 3D point despite the close proximity of the camera viewpoints. We conclude that even dense sequential input can exhibit significant color changes while our method - thanks to operating in 3D - leads to a smooth and stable color prediction.\n\n> Corresponding Question: It would be valuable to evaluate the method on temporally ordered sequences and discuss whether temporal smoothness constraints or recurrent modeling might improve consistency in dynamic or video-based applications.\n\nFor the first part, please see the answer for the first weakness above. In addition, we agree that dynamic 3D reconstruction / 4D reconstruction would be an interesting avenue for our proposed system. Given that this would require significant additional research going beyond the proposed system, we identify this as promising future work we aim to address soon.\n\n> Weakness: The training process assumes access to accurate per-image exposure metadata, which may not always be available.\n\n> Corresponding Question: The training process assumes access to per-image exposure values. How sensitive is the method to noise or inaccuracy in this metadata?\n\nThanks for the interesting question. Indeed, similar to related works, we assume to have per-image exposure given during training. To quantify the robustness of our method toward noise in the input exposure, we ran an ablation study on the HDRNeRF Dog scene. We augment the input exposure with Gaussian noise with different standard deviation levels varying from 1e-5 to 1e-2 (while exposure is normalized to be in the range (0, 1]):\n\n| Noise | PSNR  | SSIM  | LPIPS  |\n|---|---|---|---|\n| 0.0  | 41.25 | 0.993   | 0.008  | \n| 1e-5  | 41.17  | 0.993  | 0.009     | \n| 1e-4  | 40.71 |  0.992 |  0.011  |   \n| 1e-3  | 36.98 |  0.976 |  0.029  |   \n| 1e-2 | 15.70 |  0.513 |  0.537  |  \n\nWe observe that our model is robust toward noise levels with a standard deviation of up to 1e-4. For a noise level of 1e-3, results slightly worsen while still exhibiting high PSNR/SSIM/LPIPS metrics. For a standard deviation of 1e-2, the input exposure is too drastically corrupted leading to drop in performance. We added this to the final manuscript. \n\n>  Weakness: The well-exposedness and saturation weights are based on fixed formulas and hyperparameters. Although empirically effective. How about the sensitivity of the method to hand-crafted terms and the generalization in drastically different lighting distributions?\n\n> Corresponding Question: It may be helpful to explore adaptive or learned weighting schemes, or to analyze performance under extreme lighting variations to evaluate how generalizable the method is beyond the studied datasets.\n\nThank you for your suggestion. The current evaluations already contain drastic lighting variations (see e.g. Figure 2 of the supp. mat.), but we agree that evaluating even more extreme / out-of-distribution (OOD) lighting conditions are interesting to explore. To this end, we trained our method on the HDRNeRF Dog scene where input training images were only sampled with exposure values t_2 and t_4, and we report evaluation on the test set images for all exposure values t_1 - t_5, where t_1 < t_2 < t_3 < t_4 < t_5 (see also Section 4.1 of the main paper). Different from the evaluation in Table 1 of the main paper, the exposures t_1 and t_5 are not only not observed during training but even outside of the observed exposure range [t_2, t_4] which depicts an even more extreme case of OOD data. \n\n| Exposure Set | PSNR  | SSIM  | LPIPS  |\n|---|---|---|---|\n| ID: {t_2, t_4}  | 41.62 | 0.995   | 0.006  | \n| OOD: {t_3}  | 36.17  | 0.987  | 0.018     | \n| Extreme OOD: {t_1, t_5}  | 30.83 |  0.977 |  0.033  |   \n\nWe find that our model produces high-quality images for all sets, including  the extreme out-of-distribution (OOD) scenario. Quantitatively, this can be seen in particular in the high SSIM and LPIPS metrics for all exposure sets. The drop in PSNR, in particular for the extreme OOD scenario, can be explained by the fact that the PSNR metric is directly comparing per-pixel RGB values while our model never observes any reference image for such exposure values as they are even outside of the training range. We added this to the final manuscript."}, {"review_id": "b1gHFP9Gw0", "reviewer": "Reviewer_oJDR", "summary": "This work introduces neural exposure fields to model the 3D exposure, which optimizes the exposure field and scene representation together. It bypasses the need for post-processing steps or multi-exposure captures. Experimental results verify its effectiveness on challenging scenes.", "questions": "Please review the \"Weakness\".", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The paper is well-written and easy to follow.\n- The learning of the exposure field results in strong rendering performance, demonstrating improved performance on challenging scenes. \n\nWeakness:\n- It is not entirely convincing that the exposure field depends solely on the 3D point. In my understanding, exposure is also partially influenced by the viewing direction. The exposure conditioning approach proposed in Fig. 5b may therefore not be the most appropriate design. Although the method achieves better performance compared to Fig. 5a, the improvement appears to be more of an engineering modification rather than a fundamentally grounded design choice.\n- There is no parameter and time comparison. As seen in Fig. 5, the proposed method is built on HDR-NeRF, so it might be convincing to also compare the parameters and efficiency with HDR-NeRF.\n- It is recommended to replace the proposed Latent Exposure Conditioning in Fig. 5b by Fig. 5a in the ablation study, making it more straightforward to demonstrate the effectiveness of the proposed method.", "was_revised": true, "final_justification": "My main concern in the previous stage was that the exposure field depends on both the 3D position and view direction. The authors have provided further analysis to address this point. Overall, the paper presents a solid idea, comprehensive experiments, and clear writing and analysis. I therefore recommend acceptance.", "rebuttal": "Thank you for the thoughtful feedback on our submission. Describing our paper as \"well-written\" and our method as having \"strong rendering performance\" on \"challenging scenes\" is motivating for us. We address your concerns below: \n\n> It is not entirely convincing that the exposure field depends solely on the 3D point. In my understanding, exposure is also partially influenced by the viewing direction.\n\nThanks for your comment. We agree that exposure itself is view-dependent as it is a scalar that is defined per image and hence camera pose. In this work, our goal is not to reproduce input exposure, but instead learn an **optimal exposure value** per 3D point. This enables, as can be seen in the fly-through videos in the supp. mat., smooth and stable color prediction across the entire scene, while per image/camera pose exposures can vary drastically as shown in Figure 2 of the main paper. \nFurther, below we report an additional ablation study comparing our default method to our method with the modification of adding the viewing direction onto the bottleneck vector, producing similar results:\n\n| Method | PSNR  | SSIM  | LPIPS  |\n|---|---|---|---|\n| Ours | 41.25 | 0.993   | 0.008  | \n| Ours + View. Dep.  | 41.33 | 0.993  | 0.008  | \n\n > The exposure conditioning approach proposed in Fig. 5b may therefore not be the most appropriate design. Although the method achieves better performance compared to Fig. 5a, the improvement appears to be more of an engineering modification rather than a fundamentally grounded design choice.\n\nWe appreciate the comment and agree that operating in a latent space instead of on RGB color directly adds a layer of abstraction. This design choice leads to a significant performance improvement of 45% in MSE (+2.7dB in PSNR), as can be seen in Table 2 of the main paper and below in the final answer. We therefore believe that it is still a valuable contribution and design choice. Further, we note that operating in latent space is common in modern computer vision models and has led to breakthroughs (see e.g. latent diffusion in generative modeling (LDMs) or positional encoding in view synthesis (NeRF)). \n\n> There is no parameter and time comparison. As seen in Fig. 5, the proposed method is built on HDR-NeRF, so it might be convincing to also compare the parameters and efficiency with HDR-NeRF.\n\nIn the following, we report number of parameters and render time on the HDRNeRF dataset next to the training time (that is also reported in Table 1 of the main paper) for HDR-NeRF and our method:\n\n| Method | Train Time (min)  | Params (mio)  | Render Time (sec)  |\n|---|---|---|---|\n| HDR-NeRF  | 542 | 1.19   | 3.39  | \n| Ours  | 11 | 154.62  | 0.07     | \n\nWe find that our model trains ~49x faster and renders ~48x faster. We achieve this large training and test time speed up by incorporating a multi-resolution hash grid backbone (see also [1] and [2]). Compared to HDR-NeRF’s MLP architecture, this increases the number of parameters by two orders of magnitude. In most practical use cases, training and in particular test time speed is significantly more important than the parameter count which can also be seen by the large adoption of grid-based architectures since the introduction in InstantNGP [1].\n\n*[1]: Müller, Thomas, et al. \"Instant neural graphics primitives with a multiresolution hash encoding.\" ACM transactions on graphics (TOG) 41.4 (2022): 1-15.*\n\n*[2]: Barron, Jonathan T., et al. \"Zip-nerf: Anti-aliased grid-based neural radiance fields.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.*\n\n> It is recommended to replace the proposed Latent Exposure Conditioning in Fig. 5b by Fig. 5a in the ablation study, making it more straightforward to demonstrate the effectiveness of the proposed method.\n\nIn the ablation study in Table 2 of the main paper, we find that our default model improves over 45% in MSE compared to our model without the latent exposure conditioning  (see also answer 2)."}, {"review_id": "koKFqzKSPk", "reviewer": "Reviewer_Lgpm", "summary": "The paper tackles the problem of learning a NeRF representation in scenes where input images suffer from severe inconsistencies between captures (e.g., exposure differences), which can break multiview consistency and cause artifacts. To address this, the authors propose learning a 3D exposure field defined per 3D point rather than per image, as done in previous works. This approach enables exposure to be optimized jointly with the scene representation, resulting in 3D-consistent and well-exposed renderings without requiring post-processing or reference views. The method achieves state-of-the-art quantitative results by a large marginand is also the fastest to train, with a 3× speedup over the second fastest method.", "questions": "The 3D exposure field is a core contribution of the paper and is designed to improve consistency and smoothness across viewpoints—qualities that are best appreciated through video sequences rather than per-frame metrics. The included videos already show high-quality results, but I believe the paper would benefit from more targeted qualitative comparisons to help isolate the specific impact of the 3D exposure field.\n\nFor instance, a side-by-side video comparison between:\n\n-the proposed 3D exposure field,\n-and a static embedding from test/train images\n\ncould highlight how much the 3D modeling contributes to temporal coherence, scene clarity, and exposure stability—especially in challenging HDR scenes.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strenghts\n\n-State-of-the-art results and speed: The proposed method achieves the best quantitative results across multiple benchmarks and is also the fastest to train—3× faster than the second-best approach.\n\n-Simple but effective ideas: The core contribution (a 3D neural exposure field) is conceptually simple yet elegant and leads to meaningful improvements.\n\n-Well-justified regularization: The introduction of well-exposedness, saturation, and smoothness regularization terms is intuitive. These terms help the model infer more realistic exposure levels per 3D point, effectively guiding the learning of correct appearance across varying exposure conditions. Ablation studies confirm their impact.\n\n-Weaknesses\nIt is unclear why the authors compare to original NeRF rather than ZipNeRF in Table 1, especially since ZipNeRF is their actual architectural backbone.", "was_revised": true, "final_justification": "I like the paper and I have no further questions, I confirm my rating as 5.", "rebuttal": "Thank you for your thorough review. We are encouraged by your description of our method as the one with \"the best quantitative results across multiple benchmarks\" while it \"is also the fastest to train\", and are delighted to hear that our core contributions are \"simple but effective ideas\". We address concerns in the following:\n\n> It is unclear why the authors compare to original NeRF rather than ZipNeRF in Table 1, especially since ZipNeRF is their actual architectural backbone.\n\nThank you for your insightful comment. In Table 1, we refer to the original NeRF and report it as it is part of the common benchmark (see also [1] and [2]). However, we agree that this comparison would benefit from also reporting ZipNeRF which produces the following results:\n\n| Method | PSNR  | SSIM  | LPIPS  |\n|---|---|---|---|\n| NeRF  | 14.24 |  0.539   | 0.402  | \n| ZipNeRF  | 19.37  | 0.703  | 0.133     | \n| Ours  | 40.45 |  0.986 |  0.017  |   \n\nWe updated Table 1 of the main paper.\n\n[1] *Huang, Xin, et al. \"Hdr-nerf: High dynamic range neural radiance fields.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.*\n\n[2] *Cai, Yuanhao, et al. \"Hdr-gs: Efficient high dynamic range novel view synthesis at 1000x speed via gaussian splatting.\" Advances in Neural Information Processing Systems 37 (2024): 68453-68471.*\n\n> The 3D exposure field is a core contribution of the paper and is designed to improve consistency and smoothness across viewpoints—qualities that are best appreciated through video sequences rather than per-frame metrics. The included videos already show high-quality results, but I believe the paper would benefit from more targeted qualitative comparisons to help isolate the specific impact of the 3D exposure field. For instance, a side-by-side video comparison between: -the proposed 3D exposure field, -and a static embedding from test/train images could highlight how much the 3D modeling contributes to temporal coherence, scene clarity, and exposure stability—especially in challenging HDR scenes.\n\nThank you for your time to analyse not only the paper but also the supp. mat. which includes the video fly-throughs you are referencing. We find your comment encouraging and agree that such a qualitative comparison would be helpful for the reader. Unfortunately, we are not allowed by official NeurIPS guidelines to attach any image or video material to the rebuttal, but we will include this in the final version and a project page."}, {"review_id": "lQpDbcFs8h", "reviewer": "Reviewer_wXZ9", "summary": "This paper proposes Neural Exposure Fields (NExF), a NeRF-based framework that robustly handles exposure-varying input images by learning the exposure and scene jointly. Specifically, the key designs of the proposed method include (1) the latent exposure conditioning that conditions the bottleneck latent vector (rather than output radiance) on the log exposure; (2) a neural exposure field that encodes exposure in the 3D space, and a carefully designed loss function suite for optimization. The results demonstrate superior performance compared to baselines.", "questions": "# Major Questions\n1. Please provide a reasonable explanation of the design of latent exposure conditioning. An ablation study comparing latent exposure conditioning and explicit exposure conditioning can further validate the method.\n2. Please provide a reasonable explanation of the design of 3D exposure modeling. An ablation study is also preferred.\n3. Please provide comparisons with the Bilarf baseline.\n\n# Minor Questions\n1. Positional encoding of the neural fields: Does the paper use Instant-NGP hashgrid as the positional encoding (as in Zip-NeRF), or the sinusoidal positional encoding? This implementation detail should be mentioned.\n2. Ablation study: I don't quite understand the \"w/o view MLP\" ablation in Tab. 2. Isn't the view-dependent MLP a standard design in all kinds of NeRF works? Is there anything special in your design?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n1. Good paper writing: The paper is generally well-written and easy to follow. I appreciate the author's efforts in illustrating key design components (Figs. 4 and 5), which greatly improve readability.\n2. Comprehensive design and good results: The proposed framework is carefully and reasonably designed. The multi-objective loss function for the neural exposure field comprehensively considers different factors that may affect exposure and is validated by the ablation studies. Overall, this comprehensively designed framework yields a superior result compared to the baselines.\n\n# Weaknesses\n1. Explanations of latent exposure conditioning. Although the authors claimed that their proposed latent exposure conditioning is more effective than the previous work's exposure conditioning, the authors did not provide sufficient explanations and evidence for it. The bottleneck latent vector lies in the latent space without explicit physical meaning, so directly adding a physical parameter (the log exposure) to it does not make a lot of sense, compared to previous work's explicit exposure conditioning, which is more interpretable.\n2. Explanations of 3D exposure modeling. This proposed framework encodes exposure in the 3D space, which is also weird. In my opinion, exposure is a camera-dependent property, rather than space-dependent, since it is associated with the camera image processing. For a single image, the camera exposure on all pixels should be consistent, but modeling exposure in 3D may cause inconsistency.\n3. Missing baseline: The paper has cited the recent work Bilarf (and also evaluates on its dataset), but does not compare with Bilarf.", "was_revised": true, "final_justification": "My questions and concerns are mostly resolved, so I decided to raise the score to 5.", "rebuttal": "Thanks for the detailed review. We are encouraged that you find our paper \"well-written and easy to follow\" and our method to be \"carefully and reasonably designed\" with \"superior result[s] compared to the baselines\". We address concerns in the following:\n\n> Weakness: Explanations of latent exposure conditioning. Although the authors claimed that their proposed latent exposure conditioning is more effective than the previous work's exposure conditioning, the authors did not provide sufficient explanations and evidence for it. The bottleneck latent vector lies in the latent space without explicit physical meaning, so directly adding a physical parameter (the log exposure) to it does not make a lot of sense, compared to previous work's explicit exposure conditioning, which is more interpretable.\n\n> Corresponding Question: Please provide a reasonable explanation of the design of latent exposure conditioning. An ablation study comparing latent exposure conditioning and explicit exposure conditioning can further validate the method.\n\nWe appreciate the comment and agree that operating in a latent space instead of RGB color directly adds a layer of abstraction. This design choice leads to a significant performance improvement of 45% in MSE (+2.7dB in PSNR), as can be seen in Table 2 of the main paper. We therefore believe that it is still a valuable contribution and design choice. To investigate additional potential design choices, we ran the following ablation study on the HDRNeRF Dog scene:\n\n| Method | PSNR  | SSIM  | LPIPS  |\n|---|---|---|---|\n| Ours | 41.25 | 0.993   | 0.008  | \n| Ours without Latent but with Explicit Exposure Conditioning  | 40.14 | 0.989  | 0.020  | \n| Ours without Exposure Log Transform  | 34.18 | 0.965  | 0.021  | \n| Ours without Exposure Conditioning  | 18.38 | 0.753  | 0.103  | \n\nWe find that all our design choices lead to improved performance. We added this to the final version. Further, we note that operating in latent space have become common in modern computer vision models and has led to breakthroughs in related subfields (see e.g. latent diffusion in generative modeling (LDMs) or positional encoding in view synthesis (NeRF)).\n\n\n> Weakness: Explanations of 3D exposure modeling. This proposed framework encodes exposure in the 3D space, which is also weird. In my opinion, exposure is a camera-dependent property, rather than space-dependent, since it is associated with the camera image processing. For a single image, the camera exposure on all pixels should be consistent, but modeling exposure in 3D may cause inconsistency.\n\n> Corresponding Question: Please provide a reasonable explanation of the design of 3D exposure modeling. An ablation study is also preferred.\n\nThanks for your comment. We agree that exposure itself is view-dependent as it is a scalar that is defined per image and hence camera pose. In this work, our goal is not to reproduce input exposure, but instead learn an **optimal exposure value** per 3D point. This enables, as can be seen in the fly-through videos in the supp. mat., smooth and stable color prediction across the entire scene, while per image/camera pose exposures can vary drastically as shown in Figure 2 of the main paper. \nFurther, below we report an additional ablation study comparing our default method to our method with the modification of adding the viewing direction onto the bottleneck vector, producing similar results:\n\n| Method | PSNR  | SSIM  | LPIPS  |\n|---|---|---|---|\n| Ours | 41.25 | 0.993   | 0.008  | \n| Ours + View. Dep.  | 41.33 | 0.993  | 0.008  | \n\n\n> Weakness: Missing baseline: The paper has cited the recent work Bilarf (and also evaluates on its dataset), but does not compare with Bilarf.\n\nThanks for the comment. Please note that in our benchmarks, we focus on reporting results for state-of-the-art methods that are comparable to our model. For example, Bilarf, as discussed in the main paper (see L.99 - L.101), requires, in contrast to our method and baselines, a target reference image produced with HDR software as input, and hence is not directly comparable. To test the performance of our method on in-the-wild data (without any target reference image as input), we report a qualitative result on the Bilarf dataset in Figure 8 of the main paper.\nDespite not being directly comparable in terms of input (see above), we nevertheless ran our method on the Statue scene of the Bilarf dataset producing the following comparison:\n\n| Method | PSNR  | SSIM  | LPIPS  |\n|---|---|---|---|\n| Bilarf | 23.33 | 0.826   | 0.137  | \n| Ours  | 24.23 | 0.828  | 0.152  | \n\nWe find that our method overall performs slightly better than Bilarf on its own benchmark. Further, it is worth noting that Bilarf optimizes per-image 3D grids and requires two stage optimization, leading to significant memory requirements for larger captures and longer optimization times. We added this extended discussion to the final version. \n\n\n> Minor question: Positional encoding of the neural fields: Does the paper use Instant-NGP hashgrid as the positional encoding (as in Zip-NeRF), or the sinusoidal positional encoding? This implementation detail should be mentioned.\n\nThanks. We use a multi-resolution hash grid backbone similar to Instant-NGP as positional encoding. We clarified this in the final version.\n\n> Minor question: Ablation study: I don't quite understand the \"w/o view MLP\" ablation in Tab. 2. Isn't the view-dependent MLP a standard design in all kinds of NeRF works? Is there anything special in your design?\n\nThanks for the comment. The method name reads \"w/o Our View. MLP\" not \"w/o View. MLP\", i.e. you missed the \"Our\" of the description. In the referenced Table 2 of the main paper, we compare our proposed view-dependent MLP architecture to the view-dependent architecture proposed in ZipNeRF, finding that our proposed architecture leads to improved performance. For more details on this, please see L.121 - L.125 of the main paper."}]

Author rebuttal:

Meta-review: This paper proposes a novel method to jointly estimate both a scene's representation and its exposure enabling reconstruction of high dynamic range scenes captured in the wild. It received final ratings of 2 x borderline accept and 2 x accept from four expert reviewers. The reviewers appreciated the work for its novelty and strong quantitative performance. The reviewers concerns were sufficiently addressed during the reviewer-author discussion phase. The AC concurs with the reviewers' consensus and recommends acceptance. Congratulations! The authors should include the changes that they have promised in their final camera ready paper.
Decision: Accept (poster)