See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction
Authors: Wuyuan
Zhiqiang Yan
Yigong Zhang
Xiang Li
Jian Yang
Venue: NeurIPS 2025 poster ()
Keywords: occupancy prediction
nighttime scenes
illumination-driven modeling
Primary area: applications

Abstract: Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on daytime benchmarks but struggle in nighttime scenarios due to limited visibility and challenging lighting conditions. To address these challenges, we propose LIAR, a novel framework that learns illumination-affined representations. LIAR first introduces Selective Low-light Image Enhancement (SLLIE), which leverages the illumination priors from daytime scenes to adaptively determine whether a nighttime image is genuinely dark or sufficiently well-lit, enabling more targeted global enhancement. Building on the illumination maps generated by SLLIE, LIAR further incorporates two illumination-aware components: 2D Illumination-guided Sampling (2D-IGS) and 3D Illumination-driven Projection (3D-IDP), to respectively tackle local underexposure and overexposure. Specifically, 2D-IGS modulates feature sampling positions according to illumination maps, assigning larger offsets to darker regions and smaller ones to brighter regions, thereby alleviating feature degradation in underexposed areas. Subsequently, 
3D-IDP enhances semantic understanding in overexposed regions by constructing illumination intensity fields and supplying refined residual queries to the BEV context refinement process. Extensive experiments on both real and synthetic datasets demonstrate the superior performance of LIAR under challenging nighttime scenarios. The source code and pretrained models are available [here](https://github.com/yanzq95/LIAR).

See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction
Yuan Wu Zhiqiang Yan Yigong Zhang
Xiang Li Jian Yang
PCA Lab', Nanjing University of Science and Technology
National University of Singapore
Nankai University
Abstract
Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on daytime benchmarks but struggle in nighttime scenarios due to limited visibility and challenging lighting conditions. To address these challenges, we propose LIAR, a novel framework that learns illumination affined representations. LIAR first introduces Selective Low-light Image Enhancement (SLLIE), which leverages the illumination priors from daytime scenes to adaptively determine whether nighttime image is genuinely dark sufficiently well-lit, enabling more targeted global enhancement. Building on the illumination maps generated by SLLIE, LIAR further incorporates two illumination-awan compo- nents: 2D Illuminatoonguude Projection (3D-IDP), respectively tackle local underexposure and overexpo- sure. Specifically, 2D-IGS modulates feature sampling positions according to illumination maps, assigning larger offsets darker regions and smaller ones brighter regions, thereby alleviating feature degradation in underexposed areas. Subsequently, 3D-IDP enhances semantic understanding in overexposed regions by constructing illumination intensity fields and supplying refined residual queries to the BEV context refinement process. Extensive experiments on both real and synthetic datasets demonstrate the superior performance of LIAR under challenging nighttime scenarios. The source code and pretrained models are available here
Sampling (2D-IGS) and 3D Illumination-driven
Introduction
task in autonomous driving, as allows vehicles to perceive their surroundings and make informed decisions. Recently, vision-based occupancy prediction [12 [8 26 31 has attracted growing interest due to its ability to estimate the spatial layout of occupied regions together with their semantic labels. Although existing methods perform well under daytime conditions, their performance drops significantly in nighttime scenes. The need for reliable perception in such low-light environments underscores the importance of advancing nighttime occupancy prediction. However, this task remains particularly challenging, as limited visibility and complex lighting conditions can severely degrade the quality of visual inputs.
Understanding the 3D structure of the environment [5 16 52 55
As illustrated in Fig. [a), nighttime images not only exhibit low visibility but also contain both derexposed and overexposed regions 23 39 57 Underexposure caused by insufficient illumination severely degrades visual features, whereas overexposure from artificial light sources, such as vehicle headlights and streetlamps, results in significant semantic deficiency in the saturated regions.
"qqual contribution. Corresponding authors.
*CA Lab, Key Lab Intelligent Perception and Systems for Diggh-Diensional Information Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Others
Underexpostre
Feanure degradation
Overesposure
defiliency
Ours
Lesming Ilhmination Repeesentations
GT
FlachOce
Ours
Imege
Illumination
(a) Challenging lighting conditions
(b) Pipeline comparison
Figure I: (a) Nighttime images often suffer from both underexposure and overexposure, resulting in feature degradation and semantic deficiency. (b) Our approach learns illumination-awar representa- tions enhance both the fundamental 2D and 3D stages.
To address these challenges, we propose LIAR, novel framework that learns illumination-afiin representations to enhance and leverage nighttime visual information. LIAR introduces a Selective Low-light Image Enhancement (SLLIE) module, which first evaluates the global illumination of nighttime image using priors derived from daytime scenes. This selective strategy ensures that only genuinely low-light images are enhanced, enabling the model focus on challenging inputs while avoiding over-amplification of well-lit images that could otherwise lead to contrast degradation or overexposure. As shown in Fig. hb), building on the illumination maps produced by SLLIE, LIAR components, 2D coumination-guideee Sampling (2D-IGS)
further incorporates two illumination-awwe and 3D Illumination driven Projection (3D-IDP), D) to respectively address underexposure and overexpo- sure. In the 2D occupancy stage, 2D-IGS leverages illumination maps to adaptively generate feature sampling points, assigning larger offsets to darker regions and smaller offsets to brighter regions. This approach enables the model effectively compensate for underexposed regions by aggregating semantic cues from eeequuatelyeepposee regions, thereby mitigating feature degradation and improv- ing feature quality. In the 3D occupancy stage, 3D-IDP constructs 3D illumination intensity fields to refine the projection process by placing greater emphasis on overexposed regions. This targeted strategy mitigates the adverse effects of semantic deficiency caused by overexposure. Concurrently, these three modules enable LIAR to effectively address both global and local illumination challenges, improving occupancy prediction in complex nighttime environments.
In summary, our contributions are summarized as follows:
To the best of our knowledge, occupancy prediction framework tailored to the challenges of nighttime environments.
the first propose LIAR, novel illumination-driven
We introduce three key components: SLLIE, 2D-IGS, and 3D-IDP. SLLIE enables global yet targeted image enhancement, while 2D-IGS and 3D-IDP respectively mitigate the negative effects of underexposure and overexposure in local regions.
Extensive experiments on both real and synthetic datasets demonstrate the superiority of our approach, achieving up 7.9-point improvement over the second- besttd-best method. Source code and pretrained models are released for peer research.
2 Related Work
Vision-based Occupancy Prediction. Recently, vision-based 3D occupancy prediction has attracted
growing interest, with studies exploring both supervised and unsupervised learning paradigms [12 31
32
68). Among supervised approaches, MonoScene is pioneering method for monocular occupancy prediction. BEVDet [II] adopts the Sitt-Spla-Shhooe (LSS) (34 for view transformation.
49
64
BEVDet4D further explores the temporal fusion strategy by fusing history frames. Building mechanism for memory-efficient occupancy prediction. In addition, several transformer-based methods have been proposed. For instance, BEVFormer [25] utilizes spatiotemporal transformers to construct BEV features. SparseOce [27 takes the first step explore the fully sparse architecture. For unsupervised methods [13 66), SelfOce
upon the BEVDet [1] 20 21 series, FlashOce [63] introduces a
[13 and OccNeRF [66] are two representative methods that employ volume

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Elrrart
SILIE
2D bncodar
3D-DPP
Predictor
JGS
Allicid
Pegesta Prizi Saaplc Pois.
Solective Low Low-ligtt Enhancement
3D Illumination iininattooo-riveen Projection
Retines Decompusttion
Nightedi--nn Day
X
Elenisatin Irenary
sag (
No X
Aptrption
20 Illuminttion-pe Sampling
Rackal Query !
Feature Sampling
Cintce Prjectcs
Figure 2: Pipeline of LIAR. The input X is initially processed by SLLIE generate illumination map I and an enhanced image X. Using I, the 2D-IGS module modulates feature sampling to enhance degraded features underexposed regions, while the 3D-IDP module constructs 3D illumination intensity fields to compensate for deficient semanties in overexposed regions. Finally, prediction head is deployed to output the occupancy.
rendering to generate self-supervised signals. Recently, Gaussian- representations have emerged as powerful approach for 3D scene modeling. For example, GaussianTR aligns Gaussian features with foundation models, achieving state-of-the-an zero-shot performance.
6
Low-light Image Enhancement. Low-light image enhancement 8
36
48
57
aims to
improve the brightness and contrast. Histogram Equalization 355 along with its variant CLHE
35 is classical method for enhancing the global contrast of images. However, their effectiveness diminishes in the presence significant image noise or non-uniform illumination. Retinex-
methods (36) offer an alternative strategy. Recently, network-based Retinex methods [2] (28 36
46 combine convolutional neural networks with Retinex theory to achieve improved accuracy, Nevertheless, in real-world nighttime driving scenarios, artificial light sources often cause spatially uneven illumination 23 39], To address this issue, LCDPNet 339 leverages local color distributions
correct illumination imbalance, while CSEC 23 models color distribution shifts enhance image quality. Different from the aforementioned methods. we do not uniformly treat all nighttime images as requiring enhancement. Instead, selectively enhance images that suffer from poor illumination, as indiscriminate enhancement may degrade the visual quality of already well-lit ones.
View Transformation. Transforming 2D image features into 3D space is crucial step in 3D> perception tasks (58) Bottom-up methods (20 21 Pul
rely on depth estimation 43) [66 to
project multi-view features into the BEV space, paradigm first introduced by LSS (34 However, the resulting BEV features are typically sparse, posing challenges for tasks that require dense spatial representations. In contrast, top-down transformer-based methods 22 25 47 generate dense BEV features, but their lack of explicit geometric constraints makes them vulnerable to occlusions and depth inconsistencies. Recent studies BI 655 attempt integrate the strengths of both paradigms. For instance, FB-OCC (24) leverages LSS -SS-genertted features to initialize transformer queries, while COTR 31 extends this strategy by employing compact occupancy representations to preserve rich geometric information. Nevertheless, these methods primarily address view transformation from projection- based perspective, neglecting the influence of environmental factors such as illumination.
3 Method
3.1 Overview
As illustrated in Fig, our LIAR comprises a SLLIE, 2D encoder, 2D-IGS, 3D-IDP and predictor. The pipeline begins with SLLIE, which takes nighttime image X input and outputs an illumination map I along with enhanced image X. The 2D encoder then extracts image feature F Fing from X.

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Guided by the spatial distribution of I, 2D-IGS performs adaptive feature sampling on F Fing. resulting in illumination-awar feature igs Subsequently, 3D-IDP integrates I and F igs to project features into 3D space. Specifically, DepthNet Φ is employed to generate context feature Fax and depth prediction D, which are processed through BEVPooling to produce context query Q. Meanwhile, 3D illumination intensity field S is derived from I to modulate deformable querying between Q and F Fax- Finally, the resulting BEV feature bev is passed to the predictor for occupancy prediction.
3.2 Selective Low-light Image Enhancement
Retinex Decomposition. Based on the Retinex theory the Retinex-enhanced image X enh is formulated as:
19 given a nighttime image X E R3xHxw
Xanh X CI, I (0,
(I)
where I IR Ix represents the illumination map and Ø denotes element-wis division. To accurately estimate from the RGB input, we adapt a stage-wise estimation strategy using self- calibrated module from SCI 30) Specifically, I is derived from the input X via cascaded learning process in a self-supervised manner. To reduce training overhead, we pretrain this module on the
nighttime subset of the nuScenes dataset (T and freeze weights within the LIAR framework.
Selective Mechanism. Leveraging the estimated illumination map I, we define an illumination factor X(I) to quantify the overall image brightness:
X(I)=
Σ I(u,v), (u,D)R
|
where Ω denotes the set of all pixel coordinates in the image. As illustrated in Fig. we assume that daytime images generally exhibit appropriate illumination and can thus serve as a natural reference for well-exposed scenes. Notably, certain nighttime images yield X(T) values comparable to those of daytime images, indicating that not all nighttime images suffer from insufficient illumination. Based on this observation, nighttime images can be categorized into low-light and well-lit subsets. To avoid unnecessary enhancement, only low-light images should be processed, while well-lit ones remain unchanged. To this end, we aim determine an illumination threshold 1" that effectively separates low-light (A(1 (T) S (*)) from well-lit ones (A(T)= > (*). Following the principle of maximum inter-class formulate the threshold selection
optimization problem. For candidate threshold t, 1et wa (t) and w1 (t) = 1 wo(t) denote the proportions of images whose illumination respectively. Let vo(() and (1(t) denote the corresponding mean illumination values for the two groups. The global mean illumination is computed as:
variance K3
factor satisfies X(I) and A(I)
PT = wo()) + wi (1),
(3)
and the inter-class variance is given by:
(!) = (I) (1) uT)² + wi (1( (mi 1r)
(4)
The optimal threshold t" is obtained by maximizing the inter-class variance:
1" = arg max (1).
(5)
This threshold separates low-light nighttime images from well-lit ones, enabling selective enhance- ment while preventing over- ver-processing, The enhanced output X is given by:
X enhy X.
if A(T) S otherwise.
x
(6)
3.3 2D Illumination-guided Sampling
As illustrated in Fig. 2 the 2D-IGS module enhances 2D image feature representations by learning adaptive sampling points from the illumination maps. Specifically, given an illumination map we first apply a lightweight guidance network &G to downsample its resolution, producing I' with the

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4p.
12
Surple
0.4
Nop lanea
Donety
the Hinggue
0.2
Bie
40.
40.6
043
0455
A(F)
0.16
0.90
Visualization of sampling points
(b) Officets distribution
Figure 3: Distributions of the illumi-
Figure 4: (a) Visualization of sampling points in low-light
nation factor night and day scenes.
regions. (b) Statistical analysis of the offset distribution.
same resolution as the image feature F img- Subsequently, we compute the illumination guidance map by applying min-max normalization to the inverse of I':
F min (I' max
g
min 1I'
Next, map I' to offset Ap (Ap.. Apy) and weight Aw via convolution layer, allowing illumination-avar control over the receptive field. Then, we modulate Ap using g:
AP Ap &g:
where @ denotes element-wist multiplication. Finally, these illumination-awwe parameters are then utilized in a deformable convolution (5) operation F.en()), which performs differentiable warping on the image feature F iing. The illumination-awan feature F Figs is computed as:
F igg = Facn Fing, + Ap) + Aw
F Fings
where p denotes the regular grid of pixel coordinates. Fig (() provides an intuitive visualization of this process, where the sampled points in underexposed regions generate offsets toward adequately- exposed regions. Furthermore, we perform statistical analysis investigate the relationship between the offset magnitude at each pixel and its corresponding illumination value, where denotes the pixel regions (red and yellow dots) exhibit
value in I. As shown Fig bb), pixels in adequately-expe minor offsets, while those in underexposed regions (blue dots) tend to have significantly larger offsets. These results indicate that 2D-IGS efficiently mitigates feature degradation in 2D representations.
3.4 3D Illumination-driven Projection
Illumination Modeling, For each BEV grid centered at (z, y). we first uniformly sample N, points along the vertical axis, yielding heights {= N. within predefined range. Each 3D point (r, 2z) is projected onto the 2D image plane using the camera projection matrix M E R3x4,
d, [ug Uy 1]" = M [r
2j 1]_ (1,.,,.
(10)
where (u,, 2;) are the projected pixel coordinates, and dj represents the corresponding depth. The illumination intensity at each height is then sampled from the illumination map I at position (uj, Uz). Finally, the aggregated illumination value at the BEV location (x, y) given by:
N₂ EI(s,, (1,1),
S(x,y) = N J=1
(11)
where [.] denotes the floor operation to discretize continuous coordinates into pixel indices. This aggregated S provides an informative estimation of 3D illumination distribution, enabling the model to localize overexposed regions and emphasize them during subsequent feature reasoning.
shown in Fig. 2 given Figs as input, the DepthNet TD is first employed to predict context-aware feature Far and depth D. Subsequently, BEVPooling [O] projects Fax into BEV space based on D, producing context query Q. To refine the BEV context features, propose constructing illumination intensity fields to emphasize overexposed regions. Specifically, each BEV query position (z, y) is first lifted into vertical set of N₂ points {
BEV Context Refinement.
N.
5

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Table Quantitative comparison on the Occ3D-nuSeene dataset. "If" denotes single-frame method.
"2f", 4f"" and "8f" denotes methods fusing temporal information from 2, 4 and 8 frames.
others
barrier
bicyele
motorgyel
pedestrian
traffic cone
truck
drive. surt.
other flat
sidewalk
manmade
car
Method
mioU
Venue
Train: night
Test: night
BEVDetOce (10) 11
13.22
0.0
0.0
0.0
34.5
0.0
0.0
0.0
0.1
58.7
1.0
24.1
26.0
19.7
21.1
arXiv22
FlashOcc (1f)
33
13:42
0.0
0.0
0.0
35.3
0.0
0.6
0.0
3.6
57.2
2.3
21.8
25.9
19.6
21.7
arXiv23
SparsoOce (10)
13.32
10,4
10.9
46.4
444
9.3
BOCV24
OPUS (1f)
11.32
0.2
50.9
LIAR (10)
19.27
9.7
11.2
37.7
14.8
12.2
1.1
12.5
58.7
8.5
27.2
27.2
20.1
20.6
NIPS25
BEVDetOce (20)
11
15.86
0.6
0.0
0.0
40.3
0.0
6.U
0.0
4.5
62.0
3.0
28.3
28.8
23.7
24.8
arXi21
BEVForer (2))
25
16.57
3.6
0.0
0.0
40.3
16.1
9.9
0.0
62.1
4.8
19.9
24.4
18.8
22.2
BOCV22
FlashOcc (2F)
61
18.15
5.4
0.0
41.5
9.5
10.5
0.0
17.7
63.5
1.7
26.8
28.0
25.0
24.6
arXiv23
FBOcc (2f) 24
19.79
9.3
16.3
40.0
13.6
12.5
17.6
59.4
6.8
24.5
29.7
20.0
21.9
OPUS (20) 3D
12.77
1.5
0.0
0.0
35.4
8.5
1.1
0.0
17.0
52.9
2.2
16.6
14.6
13.0
16.1
NIPS24
SparscOcc (20) P+]
14.29
12.0
1.9
7.9
37.9
9.8
9.3
0.4
16.6
46.9
4.8
21.8
11.4
9.7
9.9
BCCV24
COTR (20) 31
20.01
15.3
0.3
1.5
44,0
18.1
10.1
0.0
7.7
63.2
7.0
30.3
31.1
25.1
26.6
CVPR24
LIAR (20)
22.09
13.0
5.3
13.5
42.8
19.3
18.6
1.1
20.2
64.3
2.8
29.3
25.4
24.7
NIPS25
Troin:
night
Test: night
BEVDetOcc (If) 111
18.96
4.7
22.5
2.6
38.5
6.6
6.5
0.0
12.5
63.6
5.7
29.0
28.8
21.3
23.1
arXiv22
FlasOOcc (1f) 33
18.93
4.3
1.0
0.0
38.7
1.7
5.8
0.0
13.6
60.6
2.1
27.1
30.0
21.7
22.5
arXiv23
LIAR (10)
23.67
12.0
36.1
14.5
40.7
19.2
14.0
1.3
19.3
60.5
11.1
28.9
30.5
21.4
21.8
NIPS25
BEVDetOs: (20)
11
21.98
8.2
8.6
26.3
BEVForee (40)
13.77
3.1
19.8
0.7
44.1
16.6
14.5
0.0
22.3
35.8
5.6
13.9
3.1
5.0
BCCV22
FlashOce (25)
61
23.40
13.4
18.5
2.3
46.5
10.7
14.3
0.0
30.0
66.5
5.9
32.5
32.9
28.1
26.1
arXivv3
OPUS (8f) 40
20.28
14.0
10.4
12.1
40.4
15.2
13.0
0.0
27.9
62,9
5.0
25.5
19.8
17.1
20.6
NIPS24
SparseOcc (80)
F+
22.79
15.8
40.0
23,0
43.5
15.3
13.6
0.4
28.9
58.8
10.2
26.2
16.7
13.2
13.5
BOCV24
COTR (2n
25.17
16.0
41.5
42.1
17.4
12.5
0.0
26.6
65.2
11.2
27.0
33,7
24.8
25.6
CVPR24
(2f)
19.1
27.
24.7
NTP525
along the height dimension. For each point (z, y, 2;), its corresponding 2D coordinates are computed using the projection function P defined in Eq. 10 The residual query Q' at (xx, y) defined as:
N₂ Faca ((,, (,) Fex).
Q' d'(x,))
Σ
(12)
In this formulation, Faca denotes the deformable cross-attention module (67, which leverages the query Qz y) to sample relevant context feature F Fax around the projected coordinates. Finally, the refined BEV feature F bey is given by:
F bee Q Q' S,
(13)
where the illumination intensity field S acts as spatial weighting factor.
3.5
Training Loss
First, we employ a weighted cross-entropy loss Lae to supervise the learning of the predictor:
New
Nas
Coo
C frrm log
(14)
Σm e.'.m
W= m=1
where Nex and Nela denote the total number of voxels and classes. Here, Tv.m is the prediction logit for v-th voxel belonging class m, fu the corresponding label. The weight e class-wise balancing factor computed as the inverse of the class frequency. In addition, inspired by MonoScene B] we incorporate two auxiliary losses: Csem and L.00 which respectively regularize the semantic and geometric consistency of the predictions. Finally, the total training loss L, is formulated as:
L, aLee BL + C
seal:
(15)
where 33 and y hyper-parameters and we empirically set to 10, 0.2, and 0.2, respectively.

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Table 2: Quantitative comparison on the nuScenes- dataset under three severity levels.
harrier
bicydle
const veh.
mnnorrycl yel
pedesrian
traffic come
trailer
troek
sidewalk
manmude
vegefatioo tion
Method
mloU
Severity:
BEVDetOkc (16) 11
15.08
0.5
12.4
1.3
14.1
24.9
7.7
3.8
7.8
4.5
2.1
10.1
62.5
12.0
25.5
32.0
17.1
18.2
FlashOce (10 +
12:47
0.2
7.5
1,2
16.1
19.1
6.6
3.0
5.6
3.4
1.6
49.7
9.9
19.7
25.8
14.9
14.7
LIAR (10
22.52
3.7
19.4
12.1
24.4
31.8
11.0
14.8
16.3
14.4
11.6
17.9
67.9
21.5
34.1
38.2
21.8
22.2
BEVDeiO0e
20.22
18.2
20.4
35.6
8.3
12.4
9.3
3.4
18.3
14.4
35.6
28.0
24.9
BEVFormer (40)
14.13
0.7
20.4
33.6
4.4
8.3
11.3
7.8
10.2
18.0
14.0
17.4
11.2
7.2
8.6
FlashOcc (25)
03
21.83
19.5
35.0
14.5
11.6
14.3
14.3
4,4
19.0
21.3
33.2
33.3
28.9
25.1
OPUS
23.63
4.
22.2
14.0
16.3
15.5
13.3
13.8
12.0
24.0
34,4
29.4
21.2
23.7
SpansOce (80)
21,98
23.0
16.6
37.5
13.2
18.6
18.9
24.7
7.4
21.8
26.9
23.3
16.5
15.8
COTR (2n A3
21.36
23.6
10.2
16.0
31.4
8.2
11.1
17.1
18.5
3.3
15.0
19.0
33.0
35.9
29.4
22.5
LIAR (20)
30.66
5.1
31.6
17.2
32.2
43.8
16.7
19.8
21.5
23.9
20.0
27.6
75.5
33.1
42.5
45.0
35.7
30.1
Severity:
monderuze
REVDeiOke (10
6.1
2.8
6.0
2.2
1.0
24.8
13.6
14.8
FlashOcc
[
11.9
LIAR (10)
18.44
11.1
11.1
28.0
8.5
13.8
14.0
12.5
5.0
13.7
62.8
27.5
31.4
17.8
19.0
BEVDe10ce (20
28.8
20.5
FlashOCc +3
12.6
28.1
23.2
19.9
15.4
18.9
SpansOce
COTR (2f)
18.2
11.4
28.4
6.8
9.7
14.1
15.0
1.3
11.3
29.9
25.1
18.3
LIAR (2))
25.67
20.0
13.2
39.8
17.6
18.5
20.2
11.9
22.8
37.0
39.6
29.6
24.7
Severity:
BEVDetOce
3.2
2.4
14.5
(20
14.9
(40)
3.0
FlashOcc
SpansOOce
11.3
11.0
18.2
12.3
13.2
17.6
Experiments
Dataset. We evaluate our method on both real and synthetic nighttime scenarios. (1) Oce3D- nuScenes (38] includes 700 scenes for training and 150 for validation, with annotations spanning a spatial range of -40m to 40m along both the and axes, and -Im to 5.4m along the Z axis. The occupancy labels defined using voxels with size of 0.4m 0.4m x 0.4m, containing 17 categorics. Notably, the training and validation sets include 84 and real-world nighttime scenes, respectively. (2) nuScenes-C [5]] is synthetic benchmark that introduces eight types of data corruptions, each applied at three intensity levels to the validation set nuScenes. Among them, the Dark corruption simulates nighttime by reducing brightness and contrast and adding random noise.
Implementation Details. We present two variants LIAR: non-temporal version and temporal version that incorporates one historical frame. Both of them are built upon the FlashOcc (33 series and utilize ResNet-50 7 as the encoder. During training, we employ the Adam W optimizer (2] with learning rate of 2 x 10-4, training each model for 24 epochs. Our implementation is based on MMDetection3D and experiments are conducted on NVIDIA GeForce RTX 4090 GPUs.
4.1 Comparison with the State-of-the-Art
To comprehensively evaluate the performance under nighttime conditions, we design two experimental settings. (1) We train the models on the Occ3d-nuScenes dataset using either only nighttime data or the full dataset (daytime + nighttime), and evaluate both models on the nighttime subset (see Tab. TT.
(2) We take the model trained on the full dataset and evaluate it on the nuScenes-C (see Tab.

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Front Left
Front
Front Right
Back Left
Back
Back Right
GT
FlashOce (2f)
LIAR
Figure 5: Visual results with underexposure and overexposure on the Oce3D nuscensss dataset.
Table 3:
Ablation study of LIAR on the Oce3D-nuScenes.
2D Feature Enh.
View Transformation
LJAR
SLLIE
minU
Add Concat 2D-IGS BEVPooling 3D-IDP
baseline
13.42
14.39
ii
13.86
iii
V
13.81
iv
14.11
Input image
w/o sel. mech.
14.88
Figure 6: Adverse effect of indis-
15.31
criminate enhancement.
Comparison on Real-world Dataset. We first evaluate our LIAR on the Dcc3d-nuScenc dataset. As shown in Tab. [] LIAR consistently achieves superior performance across both training settings. Notably, when trained solely on nighttime data, our model achieves the best performance, outperform- ing the second-best approach by 5.85 mloU. Additionally, when single history frame introduced, LIAR (2f) surpasses the suboptimal method (COTR (2f) (31] by 2.08 in mloU. Furthermore, when training on the full dataset, LIAR continues to demonstrate its superiority. As illustrated in Fig. 5 LIAR (2f) delivers superior results, particularly in challenging regions such car and vegetation, where overexposure and poor visibility degrade the performance FlashOcc (2f) [63].
Comparison on Synthetic Dataset. Given the limited availability of real-world nighttime data, we further evaluate our LIAR on the nuScenes-C dataset. As shown in Tab. [2] LIAR outperforms all competing methods across all three severity levels. Under the easy setting. LIAR achieves the best performance both the single-frame and temporal fusion configurations, surpassing the second-best models by 7.44 and 7.03, respectively. Notably, under the moderate and hard settings, our single- frame model even outperforms methods that incorporate temporal fusion. For instance, under the hard setting, LIAR (If) outperforms BEVFormer (4f) and SparseOcc (8f) by 6.50 and 1.44 mloU, respectively. Fig. presents visual comparisons across all three severity levels, illustrating that our model maintains strong performance even under extremely low-light conditions.
4.2 Ablation Study
All experiments in this section are conducted on the nighttime split of the Occ3D-nuScenes dataset, with all models are trained exclusively using the cross-entropy loss.
LIAR Designs. Tab. J.bsBerreentt the ablation results of LIAR. The baseline model excludes SLLIE, 2D- IGS, and 3D-IDP, adopting BEVPooling for view transformation. Building on this, LIAR-i introduces SLLIE for low-light image enhancement, resulting in a 0.97 mIoU improvement. To investigate illumination integration for 2D feature enhancement, LIAR-i and LIAR-ii apply naive fusion methods by directly adding concatenating illumination maps with extracted 2D image features, yielding marginal gains of 0.44 and 0.39 mloU, respectively. contrast, LIAR-iv replaces these naive approaches with the proposed 2D-IGS, boosting performance to 14.11 mloU and underscoring the benefits of guided sampling. Furthermore, L.ARRv incorporates 3D-IDP to address overexposure in the 3D space, achieving an additional 1.46 mloU improvement over the bascline. Finally, LIAR-vi

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Easy
Moderute
Hard
Origin
Synthetic
FlashOce (21)
LIAR (21)
Figure 7: Visual comparisons across the three severity levels on the nuScenes- dataset.
mloU
1-0.38
7-0.5
14.50
100%
PCNN
Iniendiy
F-0.49
14,30
weiglt
Exb.. Prop.
-70%
14.00-
Baseine
Beselize
14.10
14.07
14.8
13.11
33.87
14.60
13.80-
13,90
14.40
14:19
13.70
10%
13.60
14.20
(a) Threshold SLLLIE
(b) Sampling strategy in 2D IGS
(c)
Illumination intensity in 3D IDP
Figure 8: Ablltioon study on the impact of illumination in our SLLIE, 2D-IGS, and 3D-IDP.
integrates SLLIE, 2D-IGS, and 3D-IDP sinultaneously, attaining the best overall performance with a 1.89 mloU gain over the baseline. Overall, each component positively impacts the baseline.
SLLIE. Fig. &a ()) presents the ablation study of illumination threshold in SLLIE, The baseline is LIAR-i in Tab. We manually select several illumination thresholds (0.38, 0.45, 1.00) to compare against the statistically derived optimal t*. The corresponding line graph illustrates how the proportion of enhanced nighttime images varies with different thresholds. As the proportion increases, mloU also improves, peaking at f t" However, when = 1.00. meaning all nighttime images are enhanced indiscriminately. the mloU decreases from 14.39 to 14.08. This performance drop occurs due to over- over-brightte brightening images that already possess adequate illumination, which introduces artifacts, amplifies noise, and ultimately deteriorates the overall visual quality, illustrated in Fig. 6
2D-IGS. Fig. B)b) shows the ablation study 2D-IGS. The baseline (pink bar) LIAR-iv in Tab. When the sampling module is replaced with standard DCN (5), the mloU decreases from 14.11 11 to 13.81, confirming the effectiveness of learning sampling positions according to illumination maps. Additionally, removing only the illumination guidance from the baseline model results in a 0.24 decrease in mloU, indicating that the guidance map provides essential spatial priors that modulate sampling offsets. These results validate the rational behind Illumination-guidee -uidedd sampling.
3D-IDP. Fig. B illustrates the ablation of illumination intensity field in 3D-IDP. The baseline (green bar) is LIAR-v in Tab, First, removing the illumination intensity field leads to a performance drop from 14.88 14.39 mloU. To further demonstrate the effectiveness modeling 3D illumination, we design a network that generates BEV weights as substitute, which results in a decrease of 0.54 mloU. These results underscore the positive impact explicitly modeling illumination in 3D space.
5 Conclusion
We propose LIAR in this work. Our LIAR is the first occupancy framework that addresses the chal- lenges of nighttime scenes by harnessing illumination-affined representations. LIAR first introduces SLLIE to adaptively enhance limited visibility. Subsequently, 2D-IGS and 3D-IDP are designed to weaken the adverse effects of underexposure and overexposure, respectively. Extensive experiments validate the effectiveness of our LIAR and its superiority in challenging nighttime environments.

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Limitation. We evaluate our method on both real and synthetic nighttime scenarios using Oce3D- nuScenes [3] and nuScenes-C (50), respectively. For Oce3D-nuSeenes, although it serves as the most widely adopted benchmark for occupancy prediction, its nightrime subset is relatively small, and several categories are absent. These limitations may reduce the diversity of evaluated scenarios. As for nuScenes-C, it is a synthetic nighttime dataset generated by darkening daytime images and injecting noise. Due to its synthetic nature, nuScenes-C inevitably differs from real-world data. This domain gap can be broadly categorized into two aspects: First, it retains the semantics and motion patterns of daytime scenes, which differ from actual nighttime driving scenarios. Second, it fails to replicate censor-induced degradations, such as thermal noise, motion blur, and compression artifacts.
Broader Impact. With the increasing development of vision-centric autonomous driving systems, addressing safety concerns under adverse environmental conditions has become critical for ensuring the reliability of autonomous vehicles. L.AR represents significant step toward robust 3D occupancy prediction in challenging nightrime scenarios. Given the broad applicability of this task, our approach has the potential to benefit a wide range of autonomous driving applications.
6 Acknowledgment
This work was supported by the National Science Fund of China under Grant Nos. U24A20330, 62361166670, and 62306155, and by the Technology Major Project No. 20222D0116305.
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A Technical Appendices and Supplementary Material
Table Comparison of computational cost. All metrics were measured on the NVIDIA 4090 GPU.
Method
Flops (G)
Memory (GB)
Params (M)
FPS (img/s)
mioU
BEVDetOcc 9
541.21
4.71
34.97
1.1
21.98
FlashOcc 63
439.71
2.76
58.67
7.5
23.40
OPUS 40
215.54
2.00
73.17
22.4
20.28
COTR 31
740.89
12.12
37.71
0.5
25.17
Our I.ARR (2f)
690.40
5.05
64.39
4.7
27.33
Table 5: Quantitative comparison of SLLIE and other low-light image enhancement methods. Here, y denotes the gamma correction factor, and c, T are the clip limit and tile grid size used in CLAHE. Method
Description
mloU
Gamma Correction
14
7 2.2
13.77
Histogram Equalization 35
equalize each channel
13.36
CLAHE
c 2.0, (8. 8) selective enhancement
13.84
Our SLLIE
14.39
A.1
Metries
we report the mean Intersection-over: Union (mloU) as the evaluation metric for voxel-wise occupancy predictions. Notably, the Oce3D-nuSeenes dataset the classes bus, construction vehicle, and Irailer are absent from the nighttime validation split. Therefore, the mloU is calculated over the remaining semantic categories.
Following prior works 24
31
49
63
38
A.2
Computational Cost
Tab. 4 presents the comparison of computational cost on the Oce3D-nuSeenes dataset. LIAR achieves the highest mloU, demonstrating superior performance. However, this improvement comes the cost of increased computational complexity, primarily due to the Retinex decomposition network used to generate illumination maps. Therefore, future research could focus on more efficient Retinex decomposition techniques that can be seamlessly integrated into the occupancy framework.
A.3 SLLIE Pretraining
The SLLIE module pretrained the nighttime split of the nuScenes training in self-supervised manner. Following SCI [30). we use the fidelity loss as follows:
c== - 1=1
(16)
where y is the input low-light image, x" is the is the illumination estimate at stage / and sf is the output of the self-calibrated module from previous stage. When the input image y already well- exposed (e.g., daytime image), the pseudo-target + s' becomes over-bright, thereby misleading the network to over-enhance the illumination x Consequently. Ly generates erroneous gradients that hinder the model's ability to generalize to genuine low-light scenarios. Given that the full training set comprises a large proportion daytime images (approximately 88%), jointly training the SLLIE module with the rest of the occupancy model results in optimization collapse. Therefore, we pretrain the SLLLE module on nighttime data and freeze its weights during training to ensure stability.
A.4 Comparison of SLLLE and Other Methods
We compare the low-light enhancement performance of SLLIE with Gamma Correction 14] His- togram Equalization (35), and CLAHH 35 All models are trained on the nighttime split of the dataset (38). using cross-entropy loss exclusively. As listed in Tab. 5 SLLIE
22

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2(1) > <(1>>/'53 skip
Input
Gamma Correction
Histogram Equalization
CLAHE
Our SLLIE
Figure 9: Qualitative comparison of SLLIE and other low-light image enhancement methods.
Table 6: Comparison with low-light enhance- ment methods on the OcceD-muScenes.
Table 7: Performance on the
Method
mloU
mloU
BEVDetOec 9
15.86
Method
all
day
night
BEVDetOcc + LCDPNet 339
16.18 (+0.32)
BEVDetOcc CSEC
16.23 (+0.37)
BEVDetOce (2f)
9
36.10
36.85
21.98
FlashOce (63
18.15
BEVFormer (4f)
23.67
24.17
13.77
FlashOcc + LCDPNet
18.29 (+0.14)
FlashOce (2f)
37.84
38.90
23.40
FlashOcc + CSEC
18.23 (+0.08)
OPUS (8f) 40
33.20
33.96
20.28
COTR BI
20.01
SparseOce (8f)
27
31.08
31.59
22.79
COTR LCDPNet
20.22 (+0.21)
COTR (2) -1
38.70
39.47
25.17
COTR CSEC
20.17 (+0.16)
Our LIAR (2))
39.57
40.42
27.33
Our LIAR
22.09
achieves the best performance. For instance, it surpasses the second best CLAHE (35] by 0.55 mloU. Fig. 9 presents the qualitative comparisons in challenging lighting conditions. is evident that other methods tend amplify overexposed regions and introduce excessive noise in low-light regions, whereas SLLIE effectively avoids over-enhancement and improves overall visual quality.
Moreover, conduct experiments where we apply state-of-the-art low-light enhancement methods as a preprocessing step before standard occupancy models. Specifically, we use LCDPNet (39 and CSEC 23 as low-light enhancement modules. As shown in Tab. [6] while applying enhancement methods leads to marginal improvements, our LIAR consistently outperforms existing methods. For example, our LIAR outperforms COTR LCSDPNet and COTR CSEC by 1.87 and 1.92 mloU, respectively. This highlights that the improvements are not solely attributable to low-light preprocessing, but also stem from our illumination-awan designs.
A.5 Performance on Daytime Scenes
While our method primarily designed for nighttime scenarios, we also evaluate our method on daytime scenes. Tab. 7 shows that our LIAR consistently outperforms existing methods both the daytime and full datasets, surpassing the second-best COTR (31) by 0.87 and 0.95 mloU, respectively. These results highlight the generalizability of our approach beyond nighttime conditions.
A.6 Additional Visual Results
Fig. I0 illustrates results on real-world nighttime data from the shows visual comparisons on the same scene under all three severity levels on the nuScenes-C.
while Fig.
23

---

GT
COTR
Our LIAR
Figure 10: Qualitative comparison results on the Occ3D-nuscenee dataset.
24

---

RGB&GT
Synthetic
COTR
Our LIAR
Figure 11: Qualitative comparison results on the nuScenes-C dataset across three severity levels.
25


Reviews:
[{"review_id": "dkFGLpYdaT", "reviewer": "Reviewer_gNRF", "summary": "This paper proposes a novel framework for 3D occupancy prediction in challenging nighttime driving scenarios. The method addresses visibility issues caused by underexposure and overexposure through the proposed key components.", "questions": "See Weaknesses", "limitations": "See Weaknesses", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\nThis paper shows clear motivation and well-structured methodology targeting real-world nighttime perception challenges.\n\nWeakness:\nThe computational cost is insufficiently analyzed. Only limited comparisons of latency and FLOPs are provided in the supplementary material, and only against a few baselines, making it difficult to evaluate the method's efficiency and suitability for real-time deployment.\n\nThe paper does not report performance on the full test set or on daytime scenes. While the method is designed for nighttime, it is important to demonstrate that it maintains competitive accuracy during the day to ensure overall robustness.\n\nVisual comparisons are only made against one baseline (FlashOcc), which limits the interpretability and fairness of the qualitative analysis.\n\nThe evaluation on real nighttime data is based on a relatively small subset (Occ3D-nuScenes), with most improvements shown on synthetic corruptions. This restricts the strength of real-world claims.\n\nExisting baselines are designed for general or daytime conditions and are evaluated on nighttime data without any low-light enhancement. A more fair comparison would involve combining standard occupancy prediction methods with state-of-the-art low-light enhancement techniques as a preprocessing step. Without such a baseline, it is difficult to disentangle the gains from the proposed architecture versus those from the illumination enhancement alone.\n\nThe paper lacks comparison with the latest occupancy prediction methods from recent conferences, which weakens the claim of state-of-the-art performance:\n[1] UniScene: Unified Occupancy-centric Driving Scene Generation\n[2]  STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: The computational cost is insufficiently analyzed.** \n\n**A1:** We add comparisons with additional baselines and report detailed metrics, including FLOPs, memory consumption, parameter count, and FPS. As shown in Tab. R1, our model achieves superior performance in terms of accuracy under nighttime conditions. However, it exhibits a gap in FPS and other efficiency metrics compared to several lightweight alternatives. Bridging this gap remains a valuable direction for future work.\n\nTable R1: Comparison of computational cost.\n| Model | FLOPs (G) | Memory (GB) | Params (M) | FPS (img/s) | mIoU |\n| :---- | :---- | :---- | :---- | :---- | :---- |\n| BEVDetOcc  | 541.21 | 4.71 | **34.97** | 1.1 | 21.98 |\n| FlashOcc  | 439.71 | 2.76 | 58.67 | 7.5 | 23.40 |\n| OPUS  | **215.54** | **2.00** | 73.17 | **22.4** | 20.28 |\n| COTR  | 740.89 | 12.12 | 37.71 | 0.5 | 25.17 |\n| **LIAR**  | 690.40 | 5.05 | 64.39 | 4.7 | **27.33** |\n\n\n**Q2: The proposed method is not tested in daytime scenes.** \n\n**A2:** While our method is primarily designed for nighttime scenarios, we fully agree that demonstrating robustness under daytime scenes is important. Tab. R2 shows that our LIAR consistently outperforms existing methods on both the daytime and full datasets, surpassing the second-best COTR by 0.87 and 0.95 mIoU, respectively. These results highlight the generalizability of our approach beyond nighttime conditions. We appreciate the reviewer’s insightful comment and will include the evaluation results in the revised version to provide a more comprehensive assessment.\n\nTable R2: Quantitative comparison on the Occ3D-nuScenes dataset.\n| Model | mIoU (all) | mIoU (day) | mIoU (night) |\n| :---- | :---- | :---- | :---- |\n| BEVDetOcc (2f) | 36.10 | 36.85 | 21.98 |\n| BEVFormer (4f) | 23.67 | 24.17 | 13.77 |\n| FlashOcc (2f) | 37.84 | 38.90 | 23.40 |\n| OPUS (8f) | 33.20 | 33.96 | 20.28 |\n| SparseOcc (8f) | 31.08 | 31.59 | 22.79 |\n| COTR (2f) | 38.70 | 39.47 | 25.17 |\n| **LIAR (2f)** | **39.57** | **40.42** | **27.33** |\n\n**Q3: Visual comparisons only made against the baseline method (FlashOcc).**  \n\n**A3:** Beyond FlashOcc, we intend to include additional visual comparisons with the latest state-of-the-art methods, such as COTR. While the rebuttal phase does not allow for figure uploads, these results will be provided in the revised manuscript to ensure a more thorough visual comparison.\n\n**Q4: Real-world evaluation is limited to a small subset, and most gains are demonstrated on synthetic data.**  \n\n**A4:** **(1)** *Real-world datasets with occupancy annotations are inherently limited*. Occ3D-nuScenes is the only publicly available dataset that provides annotated nighttime scenes specifically tailored for 3D occupancy prediction. Although the subset is relatively small, it remains the most widely used benchmark. **(2)** *It is worth noting that real nighttime scenes are particularly challenging due to sensor noise and uneven exposure*. These factors lead to suppressed absolute performance across all models and narrower performance margins. Nevertheless, our method consistently outperforms existing approaches under these conditions. **(3)** *We believe that future research would benefit from larger and more diverse real-world nighttime datasets*. We hope that our work can motivate and facilitate the development of more comprehensive benchmarks.\n\n**Q5: The compared methods do not incorporate low-light enhancement.**  \n\n**A5:** We conduct experiments where we apply state-of-the-art low-light enhancement methods as a preprocessing step before standard occupancy models. Specifically, following Reviewer FfZG’s suggestion (see motivation weakness 1), we use LCDPNet \\[1\\] and CSEC \\[2\\] as low-light enhancement modules. Due to limited GPU resources, all models are trained and evaluated on the nighttime split of Occ3D-nuScenes. As shown in Tab. R3, while applying enhancement methods leads to marginal improvements, our LIAR  consistently outperforms existing methods. For example, our LIAR outperforms COTR \\+ LCSDPNet and COTR \\+ CSEC by 1.87 and 1.92 mIoU, respectively. This highlights that the improvements are not solely attributable to low-light preprocessing, but also stem from our illumination-aware designs. We appreciate the reviewer’s insightful comment regarding the fairness of our comparisons. We will incorporate the additional experiments in the revised version to strengthen the completeness of our evaluation.\n\nTable R3: Comparisons on the Occ3D-nuScenes dataset. All models fuse temporal information from 2 frames.\n| Model | mIoU |\n| :---- | :---- |\n| BEVDetOcc | 15.86 |\n| BEVDetOcc \\+ LCDPNet | 16.18 (+0.32) |\n| BEVDetOcc \\+ CSEC | 16.23 (+0.37) |\n| FlashOcc | 18.15 |\n| FlashOcc \\+ LCDPNet | 18.29 (+0.14) |\n| FlashOcc \\+ CSEC | 18.23 (+0.08) |\n| COTR | 20.01 |\n| COTR  \\+ LCDPNet | 20.22 (+0.21) |\n| COTR  \\+ CSEC | 20.17 (+0.16) |\n| **LIAR**  | **22.09** |\n\n\\[1\\] Local Color Distributions Prior for Image Enhancement, ECCV 2022\\.  \n\\[2\\] Color Shift Estimation-and-Correction for Image Enhancement, CVPR 2024\\.\n\n\n**Q6:** **Lack of comparison with latest works.**  \n\n**A6: (1)** We appreciate the reviewer’s suggestion to compare with recent methods such as UniScene \\[3\\] and STCOcc \\[4\\]. These two papers were accepted to CVPR 2025 (June 13–15), after the NeurIPS 2025 submission deadline (May 15). At the time of our submission, neither paper had released full details or official code, making fair and reproducible comparisons infeasible. **(2)** Regarding UniScene, we note that it focuses on generative scene modeling from BEV layouts and does not take RGB inputs, thus diverging from the standard occupancy prediction setting targeted in our work. Nonetheless, its ability to jointly generate RGB and occupancy representations under a unified framework is conceptually inspiring, especially in scenarios with limited data. We will explicitly acknowledge this in the limitations section and discuss it as a potential direction for future work. **(3)** For STCOcc, the pretrained weights (trained with camera mask) are not publicly available. Moreover, training the model on the full dataset takes over two days with four RTX 4090 GPUs. Due to resource constraints, we train and evaluate the model on the nighttime subset. Tab. R4 shows that LIAR (2f) achieves competitive performance compared to STCOcc (16f), despite using far fewer temporal frames. \n\nTable R4: Model performance on the nighttime split of Occ3D-nuScenes.\n\n| Model | mIoU |\n| :---- | :---- |\n| STCOcc (16f) | **24.31** |\n| LIAR (2f) | 22.09 |\n\n\\[3\\] UniScene: Unified Occupancy-centric Driving Scene Generation, CVPR 2025\\.  \n\\[4\\] STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction, CVPR 2025\\.\n\n*Finally, we appreciate the reviewer's valuable and constructive feedback. Please let us know if having any further questions.*"}, {"review_id": "LLYHULnVfF", "reviewer": "Reviewer_djko", "summary": "The paper introduces LIAR, a framework designed to address the challenges of predicting occupancy in nighttime environments. This framework incorporates several innovative components:\n•\tSelective Low-light Image Enhancement (SLLIE), which uses daytime illumination priors to selectively enhance nighttime images based on their illumination levels.\n•\t2D Illumination-guided Sampling (2D-IGS) and 3D Illumination-driven Projection (3D-IDP), which help to mitigate the negative effects of underexposure and overexposure in images.\n•\tThe model achieves significant improvements in occupancy prediction in challenging nighttime scenarios by enhancing both 2D and 3D feature representations.", "questions": "Suggestions for Improvement:\n•\tError Bars and Statistical Significance: Incorporate error bars or statistical significance tests for the experimental results to better evaluate the model's robustness.\n•\tDataset Limitations: Discuss the limitations of the datasets used, especially the real-world applicability of synthetic datasets.\n•\tBroader Impact and Ethical Considerations: Address the broader societal impact of this work, including its potential applications in autonomous vehicles and its ethical implications, such as privacy concerns related to real-time surveillance in nighttime driving scenarios.", "limitations": "see Weaknesses.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1.\tNovelty and Originality: The proposed method, LIAR, introduces a new approach to handle the complex problem of nighttime occupancy prediction. The integration of illumination-aware techniques (SLLIE, 2D-IGS, and 3D-IDP) is innovative and targeted towards solving specific issues in nighttime image enhancement and occupancy prediction.\n2.\tQuality of Experiments: The paper reports extensive experiments on both real and synthetic datasets, showing that LIAR outperforms existing methods, particularly in handling underexposed and overexposed regions. This demonstrates the model’s practical applicability in real-world settings.\n3.\tReproducibility: The paper provides sufficient detail on the methodology, including an explanation of the architecture and training process. It also mentions the availability of source code and pretrained models, allowing for reproduction and further research.\n\n\nWeaknesses:\n1.\tLimited Error Reporting: The paper does not report statistical error bars, which are critical for understanding the variability and reliability of the results. This could affect the interpretation of how well the model generalizes.\n2.\tLack of Broader Context on Dataset Limitations: While the paper discusses the challenges of nighttime scenarios, it does not provide detailed insights into the limitations of the datasets used. For instance, how the synthetic datasets used in testing differ from real-world environments and how the model might perform in other, less controlled settings could be expanded upon.\n3.\tOver-reliance on Pretrained Models: The framework uses pretrained models in several components (e.g., SLLIE), which could limit the model’s generalization capability to unseen scenarios if the pretrained models are not sufficiently robust across diverse nighttime conditions.", "was_revised": true, "final_justification": "The authors has addressed my concerns. This paper can be accepted.", "rebuttal": "**To Weakness 1 and Suggestion 1: Lack of statistical error analysis.**  \n\nDue to limited GPU resources and the fact that training on the full dataset takes approximately two days, we currently report results based on the nighttime subset. We conducted five independent runs of our LIAR. As listed in Tab. R5, our model demonstrates consistent performance across multiple runs. We appreciate your emphasis on statistical rigor, which is crucial for ensuring the reliability and reproducibility of research findings. In the revised version, we will extend our statistical analysis to the full dataset, including results with error bars and statistical significance tests.\n\nTable R5: Model performance across independent runs.\n\n| Run | mIoU |\n| :---- | :---- |\n| 1 | 19.27 |\n| 2 | 19.19 |\n| 3 | 19.30 |\n| 4 | 19.26 |\n| 5 | 19.28 |\n| Mean  | 19.26 |\n| Std | 0.04 |\n\n**To Weakness 2 and Suggestion 2:** **Lack of discussion on dataset limitations and the real-world applicability of synthetic datasets.**  \n\n**(1)** We evaluate our method on both real and synthetic nighttime scenarios using Occ3D-nuScenes \\[1\\] and nuScenes-C \\[2\\], respectively. For Occ3D-nuScenes, although it serves as the most widely adopted benchmark for occupancy prediction, its nighttime subset is relatively small, and some categories are absent under nighttime conditions (see Supp. A.1). Such limitations may reduce the diversity of evaluated scenarios and affect assessment on rare classes. As for nuScenes-C, it is a synthetic nighttime dataset generated by darkening daytime images and injecting noise (see Sec. 4, line 186). Due to its synthetic nature, nuScenes-C inevitably differs from real-world data. This domain gap can be broadly categorized into two aspects: First, it retains the semantics and motion patterns of daytime scenes, which differ from actual nighttime driving scenarios characterized by sparse traffic and high illumination contrast. Second, it fails to replicate sensor-induced degradations, such as thermal noise, motion blur, and compression artifacts, all of which are commonly observed in real low-light conditions. **(2)** We acknowledge the limitations of synthetic data, but it remains highly valuable for training robust models under low-data regimes. Synthetic datasets like nuScenes-C offer scalable generation and controllable diversity, helping to compensate for the scarcity of real nighttime occupancy data. Prior works (e.g., SYNTHIA [3], GTA5 [4], CARLA [5]) have shown that synthetic data can effectively complement real-world datasets and improve model performance. We thank the reviewer for raising this important and thoughtful point. We will incorporate a more thorough discussion of dataset limitations in the revised version, and hope our reflections will encourage further efforts toward building more diverse benchmarks for nighttime occupancy perception.\n\n[1] Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving, NeurIPS 2023\\.  \n[2] Benchmarking and Improving Bird’s Eye View  Perception Robustness in Autonomous Driving, TPAMI 2025\\.  \n[3] The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes, CVPR 2016\\.  \n[4] Playing for Data: Ground Truth from Computer Games, ECCV 2016\\.  \n[5] CARLA: An Open Urban Driving Simulator, CoRL 2017\\.\n\n\n**To Weakness 3: Over-reliance on the pretrained model (SLLIE).**\n\n**(1)** The SLLIE module is pretrained and frozen due to the nature of its self-supervised loss function, which is only meaningful when applied to low-light images. Following SCI \\[6\\], we use the fidelity loss as follows: $\\\\mathcal{L}\\_f \\= \\\\sum\\_{t=1}^T \\\\left\\\\| \\\\mathrm{x}^t \\- \\\\left( \\\\mathrm{y} \\+ \\\\mathrm{s}^{t-1} \\\\right) \\\\right\\\\|^2,$ where $\\\\mathrm{y}$ is the input low-light image, $\\\\mathrm{x}^t$ is the is the illumination estimate at stage $t$ and $\\\\mathrm{s}^{t-1}$ is the output of the self-calibrated module from the previous stage. When the input image $\\\\mathrm{y}$ is already well-exposed (e.g., daytime image), the pseudo-target $\\\\mathrm{y} \\+ \\\\mathrm{s}^{t-1}$ becomes over-bright, thereby misleading the network to over-enhance the illumination $\\\\mathrm{x}^t$. Consequently, $\\\\mathcal{L}\\_f$ generates erroneous gradients that hinder the model’s ability to generalize to genuine low-light scenarios. Given that the full training set comprises a large proportion of daytime images (approximately 88%), jointly training the SLLIE module with the rest of the occupancy model results in optimization collapse. Therefore, we pretrain the SLLIE module on nighttime data and freeze its weights during training to ensure both stability and effectiveness. **(2)** Tab. R6 compares the performance of models with and without SLLIE pretraining. For the baseline model, we remove SLLIE, 2D-IGS, and 3D-IDP (see Tab. 3). Notably, even when trained jointly without pretraining, SLLIE still yields a performance gain of 0.69 mIoU over the baseline. This demonstrates that the performance gain of SLLIE stems primarily from its architectural design rather than pretraining, as evidenced by the relatively small performance gap between the pretrained and non-pretrained settings.\n\nTable R6: Ablation study of SLLIE.\n| SLLIE | Pretrain | mIoU |\n| :---- | :---- | :---- |\n| × | × | 13.42 |\n| √ | × | 14.11 (+0.69) |\n| √ | √ | **14.39 (+0.97)** |\n\n\\[6\\] Toward Fast, Flexible, and Robust Low-Light Image Enhancement, CVPR 2022\\.\n\n**To Suggestion 3: Address the broader societal impact of this work.**  \n\nThis work aims to enhance 3D occupancy perception under low-light conditions, offering clear societal benefits for autonomous driving by improving safety, particularly in challenging nighttime environments. More reliable occupancy prediction in such scenarios can help reduce accidents and facilitate the broader deployment of autonomous systems. At the same time, we acknowledge potential ethical considerations, including privacy concerns associated with real-time visual sensing in public and residential areas. We advocate for the responsible use of such technologies, guided by established data protection regulations and best practices such as anonymization, restricted data access, and secure system design.\n\n*We sincerely thank the reviewer for the professional suggestions and kind support. We hope our reply would address some concerns.*"}, {"review_id": "trynqCyrx1", "reviewer": "Reviewer_17zV", "summary": "This paper targets the well-recognized challenge of vision-based occupancy prediction in low-light (nighttime) scenarios, where limited visibility and non-uniform illumination degrade perception quality. The authors propose LIAR, a novel framework composed of three key modules:SLLIE (Selective Low-light Image Enhancement); 2D-IGS (2D Illumination-guided Sampling); and3D-IDP (3D Illumination-driven Projection).SLLIE adaptively enhances genuinely dark images using illumination priors derived from daytime scenes. 2D-IGS and 3D-IDP are designed to alleviate underexposure and overexposure effects, respectively, by modifying the feature extraction and projection stages based on spatial illumination. Experimental results on both real-world (nuScenes) and synthetic (nuScenes-C) datasets show that LIAR achieves superior performance over prior state-of-the-art approaches.", "questions": "Please see the weaknesses", "limitations": "The authors have discussed the limitations in the last section.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Novel Illumination-Aware Framework. The paper presents a well-motivated and modular framework that addresses key challenges in nighttime occupancy prediction. The use of illumination maps to guide both 2D feature refinement and 3D projection is conceptually clear and well-executed.\n2. Strong Empirical Results. LIAR consistently outperforms state-of-the-art baselines across multiple settings (1f, 2f) and datasets (nuScenes, nuScenes-C). The extensive ablation studies support the importance of each individual module (SLLIE, 2D-IGS, and 3D-IDP).\n3. Rich Visualizations. The paper provides compelling visual results, including sampling offset distributions and qualitative comparisons, which effectively convey the intuition behind the proposed modules.\n\nWeaknesses\n1. Similarity to Existing Methods (FB-BEV).The proposed BEV context refinement module is architecturally similar to the FB-BEV [1] structure, which also leverages forward and backward BEV projection. However, the paper does not conduct an ablation that isolates this similarity.\nSuggestion: Include an experiment or comparison using FB-BEV as the view transformation baseline to help clarify what performance gain is specifically attributable to the 3D-IDP design.\n2. Ambiguity in Pretraining Protocol and Fairness Concern.While LIAR is trained on the same data as baselines, the SLLIE module is pretrained to estimate illumination maps from nighttime images. This introduces an implicit supervision signal unavailable to baseline models, potentially giving LIAR an advantage in the training phase. Furthermore, details about this pretraining process are not clearly reported.Please clarify how the Retinex decomposition module is pretrained (e.g., loss, data, duration), and consider reporting performance without SLLIE or using a randomly initialized variant to isolate architectural effectiveness from pretraining benefit.\n3. Limited Evaluation Scope and Generalization Concerns.The method is only evaluated on a limited number of real nighttime scenes from nuScenes, which restricts the ability to assess its robustness across diverse conditions.Additionally, the paper does not evaluate whether LIAR degrades performance on daytime scenes, which is critical if the system is expected to work across the full diurnal cycle.Please include performance on the full nuScenes validation set (both day and night scenes) to verify that illumination-aware modules do not harm performance in well-lit conditions. And provide generalization tests to other datasets with nighttime conditions (e.g., OpenScene), or at least conduct cross-dataset evaluation (e.g., train on nuScenes night, test on OpenScene) with quantitative or qualitative results to support generalization claims.\n\nReference\n[1] Li Z, Yu Z, Wang W, et al. FB-BEV: BEV Representation from Forward-Backward View Transformations. ICCV 2023.", "was_revised": true, "final_justification": "The paper introduces LIAR, a framework designed to address the challenges of predicting occupancy in nighttime environments.  It is also competitive in daytime environments.   I maintain my score \"borderline accept\".", "rebuttal": "**Q1: The design of 3D-IDP is similar to FB-BEV and requires an ablation study to verify its contribution.**  \n\n**A1: (1)** While our design is inspired by FB-BEV \\[1\\], our 3D-IDP module introduces a fundamental difference: we construct a 3D illumination intensity field and use it as a spatial weighting mechanism for backward projection. In contrast, FB-BEV aggregates features from forward and backward projections equally, without considering the visibility degradation caused by overexposure under low-light conditions. *This design differs not only in purpose but also in computation: FB-BEV performs symmetric fusion of projections, while our 3D-IDP performs asymmetric, illumination-aware refinement*. **(2)** We provide a comparison with the FB-BEV method in our paper (see Fig. 8(c)), where the gray bar (14.39) corresponds to the view transformation strategy used in FB-BEV, and the green bar (14.88) represents our proposed method. As shown in the ablation results, our design achieves a 0.49 mIoU improvement, further demonstrating the effectiveness of the 3D-IDP module. We sincerely thank the reviewer for this valuable feedback, which helps us better highlight the novelty of our approach. We will elaborate on the differences between 3D-IDP and FB-BEV in the related work section of the revised version.\n\n\\[1\\] FB-BEV: BEV Representation from Forward-Backward View Transformations, ICCV 2023\\.\n\n**Q2: Ambiguity in SLLIE pretraining and fairness concern.**   \n\n**A2: (1)** The SLLIE module is pretrained on the nighttime split of the nuScenes training set in a self-supervised manner. We follow the stage-wise illumination estimation with a self-calibrated module structure from SCI \\[2\\]. We use the fidelity loss: $\\\\mathcal{L}\\_f \\= \\\\sum\\_{t=1}^T \\\\left\\\\| \\\\mathrm{x}^t \\- \\\\left( \\\\mathrm{y} \\+ \\\\mathrm{s}^{t-1} \\\\right) \\\\right\\\\|^2,$ where $\\\\mathrm{y}$ is the input low-light image, $\\\\mathrm{x}^t$ is the illumination estimate at stage $t$ and $\\\\mathrm{s}^{t-1}$ is the output of the self-calibrated module from the previous stage. We train the SLLIE module for 24 epochs using 4 RTX 4090 GPUs, which takes approximately 5 hours. **(2)** As discussed in our response to Reviewer djko (weakness 3), *our SLLIE module cannot be trained end-to-end on the full training set (containing both daytime and nighttime data) due to the nature of its self-supervised loss.* Specifically, the loss formulation leads to incorrect gradient signals when applied to well-lit daytime images, ultimately harming model performance. To address your concern, we compare the performance of models trained with and without SLLIE pretraining on the nighttime subset. As shown in Tab. R6, in the baseline setting where SLLIE, 2D-IGS, and 3D-IDP modules are excluded (see Tab. 3), SLLIE still yields a 0.69 mIoU improvement even when trained from scratch. This highlights that its effectiveness arises not only from pretraining but also from its inherent architectural design.\n\nTable R6: Ablation study of SLLIE.\n\n| SLLIE | Pretrain | mIoU |\n| :---- | :---- | :---- |\n| × | × | 13.42 |\n| √ | × | 14.11(+0.69) |\n| √ | √ | **14.39 (+0.97)** |\n\n\\[2\\] Toward Fast, Flexible, and Robust Low-Light Image Enhancement, CVPR 2022\\.\n\n**Q3: Limited evaluation and generalization concerns.**  \n\n**A3: (1)** As discussed with Reviewer gNRF (Q2), we also evaluate our method on the daytime subset and the full dataset. Tab. R2 shows that our model consistently outperforms existing methods without introducing degradation on daytime scenes, demonstrating strong generalization capability. **(2)** We additionally conduct experiments on the synthetic dataset nuScenes-C (see Tab. 2). Given the limited availability of real-world datasets with occupancy labels, we hope the community will contribute more diverse and comprehensive benchmarks in the future. Moreover, as mentioned by Reviewer gNRF, UniScene \\[3\\], which unifies occupancy and image generation, presents a promising direction for creating high-fidelity synthetic occupancy data for evaluation. We plan to explore this direction further as part of our future work.\n\nTable R2: Quantitative comparison on the Occ3D-nuScenes dataset.\n\n| Model | mIoU (all) | mIoU (night) | mIoU (day) |\n| :---- | :---- | :---- | :---- |\n| BEVDetOcc (2f) | 36.10 | 36.85 | 21.98 |\n| BEVFormer (4f) | 23.67 | 24.17 | 13.77 |\n| FlashOcc (2f) | 37.84 | 38.90 | 23.40 |\n| OPUS (8f) | 33.20 | 33.96 | 20.28 |\n| SparseOcc (8f) | 31.08 | 31.59 | 22.79 |\n| COTR (2f) | 38.70 | 39.47 | 25.17 |\n| **LIAR (2f)** | **39.57** | **40.42** | **27.33** |\n\n \\[3\\] UniScene: Unified Occupancy-centric Driving Scene Generation, CVPR 2025\\.\n\n*Overall, these insightful suggestions do improve our paper a lot. Please let us know if the reviewer has any further questions, thanks!*"}, {"review_id": "FobqA6Tz87", "reviewer": "Reviewer_FfZG", "summary": "This paper proposes the LIAR method, which aims to address the occupancy prediction in nighttime scenes. The LIAR method first leverages the SLLIE to enhance the input images adaptively, and then uses 2D-IGS and 3D-IDP to handle under- and over-exposures in 2D and 3D feature space, respectively, before predicting the final occupancies.", "questions": "Please refer to the weakness section.", "limitations": "The paper has discussed two limitations: the proposed method may fail under extremely irregular lighting conditions, and the paper is less comprehensively evaluated due to limited data. \n\nWhile these two points make sense, I think the first point could be elaborated to define better what extremely irregular lighting conditions refer to.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths.**\n\n+ The paper proposes an important task of occupancy prediction in nighttime scenes, which needs to handle challenging illuminations that may contain both over- and under-exposures.\n\n+ The overall idea make sense to me. \n\n+ The proposed method achieves impressive results on two datasets. Ablations are conducted to help understand the proposed methods.\n\n+ The codes are promised to be released.\n\nOverall, I think this paper proposes to address an important task with a new solution based on a reasonable idea.\n\n**Weaknesses.**\n\nI have some concerns and questions regarding the motivation, method designs and the results.\n\n***Motivation.***\n\nA main argument of this work is that existing low-light image enhancement methods may not handle nighttime well due to the uneven illumination (co-existence of over- and under-exposures). While this point makes senses, the paper misses some relevant works that directly address this issue, for example,\n\n[A] Local Color Distributions Prior for Image Enhancement, ECCV 2022\n[B] Region-aware exposure consistency network for mixed exposure correction, AAAI 2024\n[C] Color Shift Estimation-and-Correction for Image Enhancement, CVPR 2024\n\nDiscussing these methods as baselines is important to validate the proposed method, especially the novelty of SLLIE.\n\n\n\n***Method designs.***\n\n(1) The choice of pre-training SCI [29] on the nuScenes dataset [1] lacks justification and convincing evidence (there are only three non-deep learning-based enhancement methods used for comparison in the supplemental).\n\n(2) The illumination threshold seems only consider the average intensity of a whole image, which does not consider local regions. Its effectiveness on images with co-existence of over and under-exposures remains unclear.\n\n(3) The adaptive sampling is interesting but it seems that this assumes well-exposed pixels could be found in the neighborhood of the current under-exposed pixel. This may not be effective for large under-exposed regions. I wonder what is the measurement unit of the shift in Figure 4(b), and whether it would be more helpful if we consider some non-local operations? More discussions and evidences could help strengthen its contribution.\n\n(4) The pipeline contains an implicit order that first addresses underexposures in 2D then over-exposure in 3D. Are there any justifications?\n\n(5) The illumination modeling in 3D-IDP seems a uniform sampling on the illumination map. It is not clear to me why this helps localizes over-exposed regions. \n\n(6) How is the DepthNet \\Phi_D trained to make sure depth is accurate? Why is S called illumination field and how is it constructed? Why it can localize the over-exposed regions?\n\n\n\n\n***Results.***\n\n(1) I wonder what the results of daytime scenes are. Given the classifier in SLLIE, the proposed method seems able to handle both daytime and nighttime scenes. Reporting such results could demonstrate its applicability.\n\n(2) How are the competing methods trained? Are they trained on the same data? Have the proposed method compared to baselines that combine image enhancement methods and OP methods?\n\n(3) The paper compares the SLLIE with Gamma correction, Histogram Equalization (HE), and CLAHE (a variant of HE), which are simple non-deep learning-based enhancement methods. Have the proposed method compared with deep learning-based methods?\n\n(4) Have the authors ablated the deformable cross-attention module in 3D-IDP?\n\n\n\nThere are some minor issues.\n\n(1) Figure 2 is not self-contained: the diagrams of SLLIE, 2D-IGS, and 3D-IDP are quite complex with many symbols and terms (e.g., \"modulates feature sampling\" and \"illumination intensity field\") unexplained. The deformable cross-attention module [52] (F_dca) is not shown in Figure 2.\n\n(2) The related work section could be improved.\nFirst, this section could be organized better. While view transformation seems directly related to occupancy prediction, a continuous discussion before switching to low-light image enhancement may be better. Second, discussions on existing occupancy prediction works could be improved. For example, by simply saying MonoScene is a pioneering method, it is not clear what its key idea is and how it relates to this work. Based on this section, view transformation seems an important step for occupancy prediction but it may suffer from uneven illumination. If this is the case, I suggest stating it explicitly.", "was_revised": true, "final_justification": "This paper proposes a new method called LIAR for occupancy prediction in nighttime scenes. The proposed LIAR method is demonstrated to be effective for both nighttime and daytime scenes. The rebuttal addresses the concerns of the motivation, method design choices, and generalization ability with discussion and results, which are promised to be included in the revision.\n\nTherefore, I raise my rating.", "rebuttal": "**Motivation weakness 1: Insufficient discussion of recent methods specifically designed for uneven illumination correction.**\n\nFollowing the suggestion, we incorporate LCDPNet [1] and CSEC [2] as enhancement modules. Please refer to ''Reviewer gNRF - Q5'' and Tab. R3 for detailed results. We will add these results in the revision.\n\n[1] Local Color Distributions Prior for Image Enhancement, ECCV 2022.\n\n[2] Color Shift Estimation-and-Correction for Image Enhancement, CVPR 2024.\n\n**Method design weakness 1:  SLLIE pretraining is under-justified and lacks comparison with deep learning-based enhancement methods.**\n\n**(1)** Please see our response to Reviewer djko (weakness 3). **(2)** In response to your suggestion, Tab. R7 presents a comparison between our SLLIE and both traditional and deep learning-based methods. The baseline refers to the model configuration in the ablation study (see Sec. 4.2, line 219). The results show that deep learning-based methods outperform traditional ones. Nevertheless, our method achieves the highest gain (+0.97 mIoU), highlighting its effectiveness.\n\nTable R7: Comparison of SLLIE with traditional and deep learning-based enhancement methods.\n| Method | mIoU |\n| :---- | :---- |\n| baseline | 13.42 |\n| + Gamma Correction | 13.77 (+0.35) |\n| + Histogram Equalization | 13.36 (-0.06) |\n| + CLAHE | 13.84 (+0.42) |\n| + LCDPNet  | 13.90 (+0.48) |\n| + CSEC  | 13.88 (+0.46) |\n| **+ Our SLLIE** | **14.39 (+0.97)** |\n\n**Method design weakness 2: The illumination threshold overlooks local variations.**\n\nThe illumination threshold is a global-level criterion designed to select whether an image broadly suffers from low-light conditions, triggering Retinex-based enhancement via SLLIE. While it does not consider local illumination variations, our framework addresses this through the 2D-IGS and 3D-IDP modules, which are specifically designed to handle under- and over-exposed regions. *This combination ensures both efficient global selection and robust local adaptation across diverse nighttime scenarios.*\n\n**Method design weakness 3: Adaptive sampling may fail in large underexposed regions; clarification on shift units and potential for non-local strategies is needed.**\n\n**(1)** We acknowledge that adaptive sampling may be less effective in extremely large underexposed regions where no well-exposed pixels are available nearby. However, our goal is not to fully reconstruct such severely degraded areas, but to improve local regions where partial illumination is present, which is a more common scenario in real-world nighttime scenes (e.g., streetlights, headlights). **(2)** The unit in Fig. 4 is in pixels. We apologize for the confusion and will clarify this in the revised version. **(3)** As suggested, we conduct experiments by replacing our 2D-IGS module with non-local operations such as DANet [3] and CCNet [4]. For the baseline reported in Tab. R8, we remove SLLIE, 2D-IGS, and 3D-IDP (see Section 4.2, Line 217). While both DANet and CCNet yield performance gains (0.46 and 0.48, respectively), our proposed 2D-IGS still outperforms these methods. We attribute the superior performance of 2D-IGS to its effective use of illumination priors, which guide spatial feature refinement under challenging lighting conditions. Nevertheless, we find the use of non-local operations highly insightful. We will include additional discussion in the revised version. Thanks for your valuable suggestion.\n\nTable R8: Comparison of 2D-IGS with non-local modules. \n| Model | mIoU |\n| :---- | :---- |\n| baseline | 13.42 |\n| +DANet | 13.88 (+0.46) |\n| +CCNet | 13.90 (+0.48) |\n| **+Our 2D-IGS** | **14.11 (+0.69)** |\n\n[3] Dual Attention Network for Scene Segmentation, CVPR 2019.\n\n[4] CCNet: Criss-Cross Attention for Semantic Segmentation, TPAMI 2020.\n\n**Method design weakness 4: The order of addressing underexposure and overexposure requires justification.**\n\nThe ordering in our pipeline is intentionally designed based on the following two considerations: **(1)** *Recoverability of information*: Underexposed pixels retain latent details that can be restored using illumination enhancement techniques. In contrast, overexposed (saturated) regions suffer from irreversible information loss due to sensor clipping, making them difficult to recover at the 2D level. **(2)** *Directional flow of the pipeline*: Since occupancy prediction involves transforming features from 2D to 3D, enhancing underexposed regions at the 2D stage helps preserve informative visual cues for subsequent 3D reasoning. Conversely, overexposed regions are better handled in the 3D stage, where spatial and depth context can mitigate the impact of missing information.\n\n**Method design weakness 5: Why the uniform sampling of illumination modeling in 3D-IDP can help localize over-exposed regions?**\n\n**(1)** We would like to clarify that the illumination modeling in 3D-IDP does not perform uniform sampling on the 2D illumination map itself. Instead, it samples points uniformly along the vertical (z) axis in 3D space (see Sec. 3.4). These 3D points are then projected onto the 2D illumination map using camera intrinsics and extrinsics to obtain their corresponding illumination values. **(2)** The reason this helps localize over-exposed regions is that the 2D illumination map inherently reflects pixel-wise brightness, with values normalized between 0 and 1. As a result, the constructed 3D illumination intensity field naturally highlights areas with high illumination values, which correspond to over-exposed regions. This enables the model to identify and focus on those regions during refinement.\n\n**Method design weakness 6: The training of DepthNet and the definition of the illumination field S are unclear, including how $S$ is constructed and how it helps localize over-exposed regions.**\n\n**(1)** The training strategy for DepthNet follows the FlashOcc [5] series. For instance, in our LIAR (2f) setting, the depth predictions are explicitly supervised using ground-truth LiDAR signals. We will include these training details in the revised version for completeness. **(2)** We term the 3D representation of projected illumination intensities as an illumination field $S$, following the convention of naming spatially-varying quantities (e.g., radiance fields, occupancy fields) as “fields” in vision and graphics. **(3)** Please see our response to method design weakness 5 for details.\n\n[5] FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin, arXiv 2023.\n\n**Results weakness 1: Results on daytime scenes are not reported.**\n\nDue to space limitations during the rebuttal phase, we kindly refer the reviewer to Tab. R2 in our discussion with Reviewer gNRF. \n\n**Results weakness 2: The compared models lack sufficient details regarding their training protocols and do not incorporate any low-light enhancement techniques.**\n\n**(1)** We adopt two evaluation settings to ensure a fair comparison: (i) training and testing solely on nighttime data, and (ii) training on both daytime and nighttime data while testing on nighttime scenes (see Sec. 4.1, line 195). **(2)** Please see motivation weakness 1 for detail.\n\n**Results weakness 3: Have the proposed SLLIE compared with deep learning-based methods?**\n\nWe kindly refer you to method design weakness 1, where we provide a detailed comparison.\n\n**Results weakness 4: Have the authors ablated the deformable cross-attention module in 3D-IDP?**\n\nYes, and we would like to clarify that our 3D illumination field serves as a spatial weighting mechanism applied on top of the deformable cross-attention outputs. When the deformable cross-attention module is ablated, the model degrades to using standard BEVPooling for view transformation. This corresponds to the baseline (13.42 mIoU) and LIAR-v (14.88 mIoU) in Tab. 3.\n\n**Minor issues 1: Figure 2 is complex and lacks clarity, with unexplained terms and missing components.**\n\n**(1)** The term \"modulates feature sampling\" refers to the process of generating sampling offsets and weights from the illumination map, which are then used to guide deformable convolution for feature extraction on the image (see Sec. 3.3). The \"illumination intensity field\" represents the spatial weighting obtained by projecting the 2D illumination map into 3D space (see Sec. 3.4). **(2)** The deformable cross-attention is represented by the red and blue arrows in Fig. 2. Specifically, the red circles indicate the projected points from 3D space to the 2D image plane, while the blue triangles denote the offset sampling locations. The corresponding image features at these locations are aggregated using learned attention weights to produce refined BEV features. Thanks for pointing this out. We will carefully check for similar diagram-text mismatches and revise them accordingly.\n\n**Minor issues 2: Related work section needs better organization.**\n\n**(1)** We will integrate the view transformation part into the occupancy prediction section for coherence. **(2)** We acknowledge the brief description of MonoScene and will include a more detailed discussion of related methods and their relevance in the revision. Thank you for this constructive suggestion.\n\n**Limitations 1: The definition of “irregular lighting conditions” is unclear**.\n\nBy “extremely irregular lighting conditions,” we refer to scenes where strong underexposure and overexposure coexist. Examples include transitions between dark tunnels and bright headlights, or flickering lights and wet surface reflections. These challenges hinder accurate illumination estimation and may reduce the effectiveness of our enhancement modules. We appreciate the reviewer’s attention to this detail and will clarify this definition in the revised version.\n\n*We welcome further discussions on the proposed LIAR. The reviewer’s insightful comments are greatly appreciated and will significantly contribute to improving our paper. Thanks!*"}]

Author rebuttal:

Meta-review: This paper received primarily positive reviews, including two "accept" ratings, one "borderline accept" rating, and one "borderline reject" rating.

The main concerns raised in the initial reviews included issues related to the fairness of the evaluation settings, limitations of the comparison baselines and dataset, unclear details regarding the pretraining process and method designs, and a lack of analysis concerning computational costs. In response, the authors provided explanations and conducted extensive experiments to address these concerns in their rebuttal. 

Following the rebuttal and subsequent discussions, no additional issues were raised, and most reviewers leaned toward a positive assessment. The area chair agrees with the reviewers' opinions and therefore recommends accepting this paper. The authors would significantly improve their work by incorporating the additional experiments in their revision.
Decision: Accept (poster)