Title: See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction
Venue: NeurIPS 2025
Accepted: yes
Keywords: occupancy prediction

nighttime scenes

illumination-driven modeling
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

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 l earns i lluminationa ffined r epresentations. 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.

1 Introduction

Understanding the 3D structure of the environment [15, 16, 52-55] is a core task in autonomous driving, as it allows vehicles to perceive their surroundings and make informed decisions. Recently, vision-based occupancy prediction [12, 18, 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.

As illustrated in Fig. 1(a), nighttime images not only exhibit low visibility but also contain both underexposed 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.

?

Underexposure

Overexposure

GT

FlashOcc

Ours

Feature degradation

Semantic deficiency

(b) Pipeline comparison

Others

Ours

Learning Illumination-

affined Representations

(a) Challenging lighting conditions

Image

Illumination

2D feature

3D feature

Image

To address these challenges, we propose LIAR, a novel framework that learns illumination-affined 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 a nighttime image using priors derived from daytime scenes. This selective strategy ensures that only genuinely low-light images are enhanced, enabling the model to 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. 1(b), building on the illumination maps produced by SLLIE, LIAR further incorporates two illumination-aware components, 2D Illumination-guided Sampling (2D-IGS) and 3D Illumination-driven Projection (3D-IDP), to respectively address underexposure and overexposure. 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 to effectively compensate for underexposed regions by aggregating semantic cues from adequately-exposed regions, thereby mitigating feature degradation and improving 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, we are the first to propose LIAR, a novel illumination-driven occupancy prediction framework tailored to the challenges of nighttime environments.

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 to a 7.9-point improvement over the second-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, 49, 64, 68]. Among supervised approaches, MonoScene [3] is a pioneering method for monocular occupancy prediction. BEVDet [11] adopts the Lift-Splat-Shoot (LSS) [34] for view transformation. BEVDet4D [9] further explores the temporal fusion strategy by fusing history frames. Building upon the BEVDet [11, 20, 21] series, FlashOcc [63] introduces a channel-to-height 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. SparseOcc [27] takes the first step to explore the fully sparse architecture. For unsupervised methods [13, 66], SelfOcc [13] and OccNeRF [66] are two representative methods that employ volume

Retinex

Decomposition

Selective Low-light Image Enhancement

Yes

No

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P

Depth

Context Feature

igs

F

3D Illumination-driven Projection

2D Illumination-guided Sampling

Feature

Sampling

Guidance

Weights

Offsets

0.3

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0.1

Element-wise Division

Affected Grid

Refined Grid

Projection

SLLIE

2D Encoder

2D-IGS

3D-IDP

Predictor

I

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X

I

img

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igs

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bev

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I

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X

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,

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(

)

(

,

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λ

∈Ω

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Ω

∑

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*

(

)

t

λ

>

I

img

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I

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p

∆

w

g

D

Φ

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ctx

F

D

Illumination Intensity Field

S

bev

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Φ

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Projected Point

Sample Point

Night (genuinely dark)

Night (well-lit)

Day

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*

argmax

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b

t

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t

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Residual Query

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Add

Element-wise Multiplication

Aggregation

rendering to generate self-supervised signals. Recently, Gaussian-based representations [6, 17, 42] have emerged as a powerful approach for 3D scene modeling. For example, GaussianTR [17] aligns Gaussian features with foundation models, achieving state-of-the-art zero-shot performance.

Low-light Image Enhancement. Low-light image enhancement [8, 23, 36, 41, 48, 57] aims to improve the brightness and contrast. Histogram Equalization [35], along with its variant CLHE [35], is a classical method for enhancing the global contrast of images. However, their effectiveness diminishes in the presence of significant image noise or non-uniform illumination. Retinex-based 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 [39] leverages local color distributions to correct illumination imbalance, while CSEC [23] models color distribution shifts to enhance image quality. Different from the aforementioned methods, we do not uniformly treat all nighttime images as requiring enhancement. Instead, we 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 a crucial step in 3D perception tasks [58-62]. Bottom-up methods [20, 21, 34] rely on depth estimation [43-45, 56] to project multi-view features into the BEV space, a 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 [24, 31, 37, 65] attempt to integrate the strengths of both paradigms. For instance, FB-OCC [24] leverages LSS-generated 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 a projection-based perspective, neglecting the influence of environmental factors such as illumination.

3 Method

3.1 Overview

As illustrated in Fig. 2, our LIAR comprises a SLLIE, 2D encoder, 2D-IGS, 3D-IDP and predictor. The pipeline begins with SLLIE, which takes a nighttime image X as input and outputs an illumination map I along with an enhanced image ˜ X . The 2D encoder then extracts image feature F img from ˜ X .

Guided by the spatial distribution of I , 2D-IGS performs adaptive feature sampling on F img, resulting in illumination-aware feature F igs. Subsequently, 3D-IDP integrates I and F igs to project features into 3D space. Specifically, DepthNet Φ D is employed to generate context feature F ctx 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 ctx. Finally, the resulting BEV feature F bev is passed to the predictor for occupancy prediction.

3.2 Selective Low-light Image Enhancement

Retinex Decomposition. Based on the Retinex theory [19], given a nighttime image X ∈ R 3 × H × W , the Retinex-enhanced image X enh is formulated as:

where I ∈ R 1 × H × W represents the illumination map and ⊘ denotes element-wise division. To accurately estimate I from the RGB input, we adapt a stage-wise estimation strategy using a selfcalibrated module from SCI [30]. Specifically, I is derived from the input X via a cascaded learning process in a self-supervised manner. To reduce training overhead, we pretrain this module on the nighttime subset of the nuScenes dataset [1] and freeze its weights within the LIAR framework.

Selective Mechanism. Leveraging the estimated illumination map I , we define an illumination factor λ ( I ) to quantify the overall image brightness:

where Ω denotes the set of all pixel coordinates in the image. As illustrated in Fig. 3, 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 λ ( I ) 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 to determine an illumination threshold t ∗ that effectively separates low-light ( λ ( I ) ≤ t ∗ ) from well-lit ones ( λ ( I ) > t ∗ ). Following the principle of maximum inter-class variance [33], we formulate the threshold selection as an optimization problem. For a candidate threshold t , let ω 0 ( t ) and ω 1 ( t ) = 1 -ω 0 ( t ) denote the proportions of images whose illumination factor satisfies λ ( I ) ≤ t and λ ( I ) > t , respectively. Let µ 0 ( t ) and µ 1 ( t ) denote the corresponding mean illumination values for the two groups. The global mean illumination is computed as:

and the inter-class variance is given by:

The optimal threshold t ∗ is obtained by maximizing the inter-class variance:

This threshold separates low-light nighttime images from well-lit ones, enabling selective enhancement while preventing over-processing. The enhanced output ˜ X is given by:

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 I , we first apply a lightweight guidance network Φ G to downsample its resolution, producing I ′ with the

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(a) Visualization  of sampling points

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same resolution as the image feature F img. Subsequently, we compute the illumination guidance map g by applying min-max normalization to the inverse of I ′ :

Next, we map I ′ to offset ∆ p = (∆ p x , ∆ p y ) and weight ∆ w via one convolution layer, allowing illumination-aware control over the receptive field. Then, we modulate ∆ p using g :

Night  Histogram

Day Histogram Day KDE Density where ⊙ denotes element-wise multiplication. Finally, these illumination-aware parameters are then utilized in a deformable convolution [5] operation F dcn ( · ) , which performs differentiable warping on the image feature F img. The illumination-aware feature F igs is computed as:

( ) λ I where p denotes the regular grid of pixel coordinates. Fig.4(a) provides an intuitive visualization of this process, where the sampled points in underexposed regions generate offsets toward adequatelyexposed regions. Furthermore, we perform a statistical analysis to investigate the relationship between the offset magnitude at each pixel and its corresponding illumination value, where ι denotes the pixel value in I . As shown in Fig.4(b), pixels in adequately-exposed regions (red and yellow dots) exhibit 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 ( x, y ) , we first uniformly sample N z points along the vertical axis, yielding heights { z j } N z j =1 within a predefined range. Each 3D point ( x, y, z j ) is projected onto the 2D image plane using the camera projection matrix M ∈ R 3 × 4 :

where ( u j , v j ) are the projected pixel coordinates, and d j represents the corresponding depth. The illumination intensity at each height is then sampled from the illumination map I at position ( u j , v j ) . Finally, the aggregated illumination value at the BEV location ( x, y ) is given by:

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.

BEV Context Refinement. As shown in Fig. 2, given F igs as input, the DepthNet Φ D is first employed to predict context-aware feature F ctx and depth D . Subsequently, BEVPooling [10] projects F ctx into BEV space based on D , producing context query Q . To refine the BEV context features, we propose constructing illumination intensity fields to emphasize overexposed regions. Specifically, each BEV query position ( x, y ) is first lifted into a vertical set of N z points { z j } N z j =1

i

Sample Direction

y

i

i

i

∈

∈

∈

[0,0.4]

(0.4,0.6]

[0.6,1.0]

along the height dimension. For each point ( x, y, z j ) , its corresponding 2D coordinates are computed using the projection function P defined in Eq. 10. The residual query Q ′ at ( x, y ) is defined as:

In this formulation, F dca denotes the deformable cross-attention module [67], which leverages the query Q ( x, y ) to sample relevant context feature F ctx around the projected coordinates. Finally, the refined BEV feature F bev is given by:

where the illumination intensity field S acts as a spatial weighting factor.

3.5 Training Loss

First, we employ a weighted cross-entropy loss L ce to supervise the learning of the predictor:

where N vox and N cla denote the total number of voxels and classes. Here, r v,m is the prediction logit for v -th voxel belonging to class m , ˆ r v,m is the corresponding label. The weight c is a class-wise balancing factor computed as the inverse of the class frequency. In addition, inspired by MonoScene [3], we incorporate two auxiliary losses: L sem scal and L geo scal , which respectively regularize the semantic and geometric consistency of the predictions. Finally, the total training loss L t is formulated as:

where α , β and γ are hyper-parameters and we empirically set to 10, 0.2, and 0.2, respectively.

4 Experiments

Dataset. We evaluate our method on both real and synthetic nighttime scenarios. (1) Occ3DnuScenes [38] includes 700 scenes for training and 150 for validation, with annotations spanning a spatial range of -40m to 40m along both the X and Y axes, and -1m to 5.4m along the Z axis. The occupancy labels are defined using voxels with a size of 0 . 4m × 0 . 4m × 0 . 4m , containing 17 categories. Notably, the training and validation sets include 84 and 15 real-world nighttime scenes, respectively. (2) nuScenes-C [51] is a synthetic benchmark that introduces eight types of data corruptions, each applied at three intensity levels to the validation set of nuScenes. Among them, the Dark corruption simulates nighttime by reducing brightness and contrast and adding random noise.

Implementation Details. We present two variants of LIAR: a non-temporal version and a temporal version that incorporates one historical frame. Both of them are built upon the FlashOcc [63] series and utilize ResNet-50 [7] as the 2D encoder. During training, we employ the AdamW optimizer [29] with a learning rate of 2 × 10 -4 , training each model for 24 epochs. Our implementation is based on MMDetection3D [4], and experiments are conducted on 4 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. 1). (2) We take the model trained on the full dataset and evaluate it on the nuScenes-C (see Tab. 2).

GT

FlashOcc (2f)

LIAR (2f)

Front Left

Front

Front Right

Back Left

Back

Back Right

Input image

w/o sel. mech.

Comparison on Real-world Dataset. We first evaluate our LIAR on the Occ3d-nuScenes dataset. As shown in Tab. 1, LIAR consistently achieves superior performance across both training settings. Notably, when trained solely on nighttime data, our model achieves the best performance, outperforming the second-best approach by 5.85 mIoU. Additionally, when a single history frame is introduced, LIAR (2f) surpasses the suboptimal method (COTR (2f) [31]) by 2.08 in mIoU. 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 as car and vegetation , where overexposure and poor visibility degrade the performance of 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 in 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 singleframe model even outperforms methods that incorporate temporal fusion. For instance, under the hard setting, LIAR (1f) outperforms BEVFormer (4f) and SparseOcc (8f) by 6.50 and 1.44 mIoU, respectively. Fig. 7 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. 3 presents the ablation results of LIAR. The baseline model excludes SLLIE, 2DIGS, 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-ii and LIAR-iii apply naive fusion methods by directly adding or concatenating illumination maps with extracted 2D image features, yielding marginal gains of 0.44 and 0.39 mIoU, respectively. In contrast, LIAR-iv replaces these naive approaches with the proposed 2D-IGS, boosting performance to 14.11 mIoU and underscoring the benefits of guided sampling. Furthermore, LIAR-v incorporates 3D-IDP to address overexposure in the 3D space, achieving an additional 1.46 mIoU improvement over the baseline. Finally, LIAR-vi

13.84

W/O Sel. Mech.

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Moderate

Hard

Origin

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GT

LIAR (2f)

FlashOcc (2f)

14.50

13.60

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10%

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13.70

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(c) Illumination intensity in 3D IDP

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13.90

13.70

mIoU

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14.20

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13.80

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14.80

14.60

14.40

14.20

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14.07

14.09

14.39

14.08

13.81

13.87

14.11

14.39

14.34

14.88

DCN

w/o Guidance

Baseline

w/o Intensity

Learnable weight

Baseline

=0.49

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t

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Enh. Prop.

integrates SLLIE, 2D-IGS, and 3D-IDP simultaneously, attaining the best overall performance with a 1.89 mIoU gain over the baseline. Overall, each component positively impacts the baseline.

SLLIE. Fig. 8(a) presents the ablation study of illumination threshold in SLLIE. The baseline is LIAR-i in Tab. 3. We manually select several illumination thresholds t (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, mIoU also improves, peaking at t = t ∗ . However, when t = 1 . 00 , meaning all nighttime images are enhanced indiscriminately, the mIoU decreases from 14.39 to 14.08. This performance drop occurs due to over-brightening images that already possess adequate illumination, which introduces artifacts, amplifies noise, and ultimately deteriorates the overall visual quality, as illustrated in Fig. 6.

2D-IGS. Fig. 8(b) shows the ablation study in 2D-IGS. The baseline (pink bar) is LIAR-iv in Tab. 3. When the sampling module is replaced with a standard DCN [5], the mIoU decreases from 14.11 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 mIoU, indicating that the guidance map provides essential spatial priors that modulate the sampling offsets. These results validate the rational behind illumination-guided sampling.

14.50 14.39 3D-IDP. Fig. 8(c) illustrates the ablation of illumination intensity field in 3D-IDP. The baseline (green bar) is LIAR-v in Tab. 3. First, removing the illumination intensity field leads to a performance drop from 14.88 to 14.39 mIoU. To further demonstrate the effectiveness of modeling 3D illumination, we design a network that generates BEV weights as a substitute, which results in a decrease of 0.54 mIoU. These results underscore the positive impact of explicitly modeling illumination in 3D space.

14.10

13.77 5 Conclusion

13.70

13.30 13.36 Low-light Enhancement Strategy We propose LIAR in this work. Our LIAR is the first occupancy framework that addresses the challenges 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.

Limitation. We evaluate our method on both real and synthetic nighttime scenarios using Occ3DnuScenes [38] and nuScenes-C [50], respectively. For Occ3D-nuScenes, although it serves as the most widely adopted benchmark for occupancy prediction, its nighttime 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 sensor-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. LIAR represents a significant step toward robust 3D occupancy prediction in challenging nighttime 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. 2022ZD0116305.

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A Technical Appendices and Supplementary Material

A.1 Metrics

Following prior works [24, 31, 49, 63], we report the mean Intersection-over-Union (mIoU) as the evaluation metric for voxel-wise occupancy predictions. Notably, in the Occ3D-nuScenes dataset [38], the classes bus , construction vehicle , and trailer are absent from the nighttime validation split. Therefore, the mIoU is calculated over the remaining 14 semantic categories.

A.2 Computational Cost

Tab. 4 presents the comparison of computational cost on the Occ3D-nuScenes dataset. LIAR achieves the highest mIoU, demonstrating superior performance. However, this improvement comes at 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 is pretrained on the nighttime split of the nuScenes training set in a self-supervised manner. Following SCI [30], we use the fidelity loss as follows:

where y is the input low-light image, x t is the is the illumination estimate at stage t and s t -1 is the output of the self-calibrated module from the previous stage. When the input image y is already wellexposed (e.g., daytime image), the pseudo-target y + s t -1 becomes over-bright, thereby misleading the network to over-enhance the illumination x t . Consequently, 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 stability.

A.4 Comparison of SLLIE and Other Methods

We compare the low-light enhancement performance of SLLIE with Gamma Correction [14], Histogram Equalization [35], and CLAHE [35]. All models are trained on the nighttime split of the Occ3D-nuScenes dataset [38], using cross-entropy loss exclusively. As listed in Tab. 5, SLLIE

CLAHE

Our SLLIE

Input

,skip

*

(

)

t

λ

>

I

Method

BEVDetOcc (2f) [9]

BEVFormer (4f) [25]

FlashOcc (2f) [63]

OPUS (8f) [40]

SparseOcc (8f) [27]

COTR (2f) [31]

Our LIAR

(2f)

all

36.10

23.67

37.84

33.20

31.08

38.70

39.57

mIoU

day

36.85

24.17

38.90

33.96

31.59

39.47

40.42

night

21.98

13.77

23.40

20.28

22.79

25.17

27.33

achieves the best performance. For instance, it surpasses the second best CLAHE [35] by 0.55 mIoU. Fig. 9 presents the qualitative comparisons in challenging lighting conditions. It is evident that other methods tend to amplify overexposed regions and introduce excessive noise in low-light regions, whereas SLLIE effectively avoids over-enhancement and improves overall visual quality.

Moreover, we 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 mIoU, respectively. This highlights that the improvements are not solely attributable to low-light preprocessing, but also stem from our illumination-aware designs.

A.5 Performance on Daytime Scenes

While our method is primarily designed for nighttime scenarios, we also evaluate our method on daytime scenes. Tab. 7 shows that our LIAR consistently outperforms existing methods on both the daytime and full datasets, surpassing the second-best COTR [31] by 0.87 and 0.95 mIoU, respectively. These results highlight the generalizability of our approach beyond nighttime conditions.

A.6 Additional Visual Results

Fig. 10 illustrates results on real-world nighttime data from the Occ3D-nuScenes, while Fig. 11 shows visual comparisons on the same scene under all three severity levels on the nuScenes-C.

GT

COTR

Our LIAR

RGB&GT

Synthetic

COTR

Our LIAR

T

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nPlease refer to the weakness section.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nPlease see the weaknesses\n\nLIMITATIONS:\nThe authors have discussed the limitations in the last section.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nSuggestions 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.\n\nLIMITATIONS:\nsee Weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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\n\nQUESTIONS:\nSee Weaknesses\n\nLIMITATIONS:\nSee Weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-04"}