Beyond Visual Reconstruction Quality: Object Perception-aware 3D Gaussian Splatting for Autonomous Driving
Authors: Renzhi Wang
Yuxiang Fu
Wuqi Wang
Haigen Min
Wei Feng
Lei Ma
Qing Guo
Venue: ICLR 2026 Poster (2026)
Keywords: Scene reconstruction
Object detection
Autonomous Driving System
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Reconstruction techniques, such as 3D Gaussian Splatting (3DGS), are increasingly used to generate scenarios for autonomous driving system (ADS) research. Existing 3DGS-based approaches for autonomous-driving scenario generation have, through various optimizations, achieved high visual similarity in reconstructed scenes. However, this route is built on a strong assumption: that higher scene similarity directly translates into better preservation of ADS behaviour. Unfortunately, this assumption has not been effectively validated, and ADS behaviour is more closely related to objects within the field of view rather than the global image.
Thus, we focus on the perception module—the entry point of ADS. Preliminary experiments reveal that although current methods can produce reconstructions with high overall similarity, they often fail to ensure that the perception module outputs remain consistent with those obtained from the original images. Such a limitation can significantly harm the applicability of reconstruction in the ADS domain. To address this gap, we propose two complementary solutions: a perception-aligned loss, which directly leverages output differences between reconstructed and ground-truth images during training; and an object zone quality loss, which specifically reinforces training on object locations identified by the perception model on ground-truth images. Experiments demonstrate that both of our methods improve the ability of reconstructed scenes to maintain consistency between the perception module outputs and the ground-truth inputs. We release code at: https://github.com/Shanicky-RenzhiWang/Perception-aware-3DGS

Published as a conference paper at ICLR 2026
BEYOND VISUAL RECONSTRUCTION QUALITY: OB- JECT PERCEPTION-AWARE 3D GAUSSIAN SPLATTING FOR AUTONOMOUS DRIVING
Renzhi Wang
Yuxiang Fu Nankai University
Wuqi Wang Haigen Min Chang's an University
University of Alberta VCIP, CS, Nankai University
Wei Feng Tianjin University
Lei Ma
Qing Guo NKIARI, Shenzhen Futian VCIP, CS, Nankai University
University of Tokyo University of Alberta
ABSTRACT
Reconstruction techniques, such as 3D Gaussian Splatting (3DGS), are increas- ingly used to generate scenarios for autonomous driving system (ADS) research. Existing 3DGS-based approaches for autonomous-driving scenario generation have, through various optimizations, achieved high visual similarity in recon- structed scenes. However, this route is built on a strong assumption: that higher scene similarity directly translates into better preservation of ADS behaviour. Unfortunately, this assumption has not been effectively validated. and ADS be- haviour is more closely related to objects within the field of view rather than the global image. Thus, we focus on the perception module-the entry point of ADS. Preliminary experiments reveal that although current methods can produce reconstructions with high overall similarity, they often fail to ensure that the per- ception module outputs remain consistent with those obtained from the original images. Such limitation can significantly harm the applicability of reconstruc- tion in the ADS domain. To address this gap, we propose two complementary solutions: perception-aligmee between reconstructed and ground-truth images during training; and an object zone quality loss, which specifically reinforces training object locations iden- tified by the perception model on ground- truth images. Experiments demonstrate that both of our methods improve the ability of reconstructed scenes maintain consistency between the perception module outputs and the ground-truth inputs. We release code at: httos: Perceptiion-waree-333 3DGS
loss, which directly leverages output differences
com/ http//Shaniikyyyea Renzh /
INTRODUCTION
3D Gaussian Splatting (3DGS) is an efficient 3D reconstruction technique that has rapidly advanced in recent years (Kerbi et al.] (023). By enabling photorealistic scene reconstruction from multi-view images, it can accurately capture complex details, directly meeting the scene generation require- ments in the field of autonomous driving (Zhou et al. 2024 Peng et al. 2025a Yang al. 2023b).
Existing 3DGS methods for traffic scenario generation in autonomous driving still follow optimiza- tion objectives similar to those in other domains. Specifically, they focus on improving overall im- age similarity through metrics such SSIM, PSNR, and LPIPS (Yan et al. 2024 Zhou al. 2024 Peng et al. 2025b). However, in the context of autonomous driving, the goal is for ADS to make the same decisions and take the same actions in reconstructed scenes it would in the original scenes. Only such reconstructions are truly useful for system development and testing. Consequently, the objectives of existing works rely on strong implicit assumption: that higher image similarity will lead to more consistent behaviour from the autonomous driving system.
"Corresponding author; ingggug eee. org

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Published as a conference paper at ICLR 2026
Origin
Loss
Obj Loss
$ Gaussian
EMD with Gaussian
OmniRe
EMD with OmniRe
We enhance perception stability in 3D reconstruction by introducing perception-based loss and object zone quality loss, ensuring that the reconstructed images produce perception results consistent with the ground truth for reliable autonomous driving applications. The red box is the bounding box detected by the perception model.
Figure 1:
However, this assumption presents notable gap in the context of autonomous driving. In ADS, the positions, scales, and categories of objects within the sensor's field of view- eww-especially non-player characters (NPCs)-have have the most significant influence on system behaviour. Yet these objects often occupy only small portion of the frame or scene, leading global similarity metrics to under- emphasize their importance during reconstruction. Some prior works (Huang et al.) 22224] Wei al.) 2025) have also recognized this issue and attempted to specifically enhance NPC characteristics, thereby partially improving perception recognition of NPCs in reconstructed scenes. Nevertheless, these approaches do not directly address the underlying gap. Even with ground-truth inputs, per- ception modules can make errors. Therefore, it is not enough to ensure that reconstructed scenes reproduce only the correctly recognized objects; exist. For example, as shown in the Figure []] the reconstruction produced by the EMD(+OmmirRe) method can even enable the perception model detect objects than in the ground-truth input. However, this is not necessarily a desirable outcome for developers who aim to enhance perception performance through scene reconstruction. Reproducing existing errors is, fact, aligned with our broader goal for reconstruction techniques in ADS: to efficiently reveal the limitations and issues of ADS perception, enabling more effective system testing and improvement.
also need to reproduce the errors that already
This gap inspires us to move beyond the quality of visual reconstruction. We introduce the concept of perception stability, which requires that the same perception model produce consistent outputs across both ground-truth and reconstructed images. By adopting perception stability as optimiza- tion objective, reconstruction methods can better align with the ultimate goal of supporting ADS development and testing.
To validate this gap, we first conduct a preliminary study multiple 3DGS methods for ADS traf- fic scene reconstruction, using different perception models. The results show that while existing methods achieve high scores on visual metrics, these improvements do not translate into higher per- ception stability. This observation highlights the limitations of optimizing solely for visual metrics and motivates us to design improvements with perception stability as the objective. To this end, as shown in Figure we propose approaches improve perception stability during reconstruc-
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tion. The first directly aligns reconstructions with perception outputs by penalizing inconsistencies in object detection results. The second focus on the visual fidelity of task-relevant object regions, encouraging accurate reconstruction of object zones detected by the perception model, even if the detection is incorrect.
Through extensive experiments, demonstrate that both of proposed improvements not only significantly enhance the perception stability of 3DGS reconstructions. module outputs on reconstructed scenes remain consistent with those the original images-but also maintain, or in some cases even improve, the overall visual quality. This highlights that it is pos- sible design reconstruction methods that are both visually accurate and functionally meaningful, supporting the development and testing of autonomous driving systems.
ennuurrinn-essuuiinng ensuring that perception
In summary, this paper makes the following contributions:
We introduce perception-awar reconstruction, principle that reconstructed scenes should maintain visual realism while preserving perceptual stability.
We propose two approaches: a perception-alinnee loss and an object-zone quality loss, which could improve perception stability when integrated into the training process. We validate our methods using large-scale experiments, demonstrating that they improve perception stability without compromising overall visual quality.
More broadly, the proposed perception-awar reconstruction principle, along with the two methods we introduce, establishes a foundation for task-consistent reconstruction in other domains, such as robotics and AR/VR, which require perception outputs the basis for downstream modules. Our approach demonstrates how reconstruction can be made both visually realistic and practically helpful for real- world applications.
BACKGROUND
Traffic Scene Reconstruction Traffic scenario generation is important research direction for autonomous driving development and testing, while reconstruction techniques enable more flexi-
ble and realistic scenarios (Nalic et al. 2020 Zhong al. 2021 Wu al.] 2024). In the early stages of research, series of NeRF-based works (Mildenhal et al. 2021 Luo el al. 2023), b, such as Block-NeRF TTaneik et al., 2022). Mega-NeRF (Turki et al. 2022). and EmerNeRF (Yngg et al. 2023a), explored and attempted to capture the characteristics of traffic scenes. With the emer- gence of 3DGS, techniques represented by StreetGaussian Yan et al. 2 DrivingGaussian (Zhou et al. 2024), and DeSiRe-GS (Peng et al.] (025a) have rapidly proliferated. To date, methods like 5*Gaussian (Huang et al. 2024), OmniRe (Chen et al. 20025) and EMD (Wei et al. 2025) lead the field in terms of reconstruction performance and scalability.
Perception Robustness in AI Systems As the first stage in many AI systems, perception plays a critical role, and its robustness and stability are essential for overall system reliability. Dong et al. 2023 proposed a robustness benchmark for autonomous driving system (ADS) perception modules based on Waymo (Sun et al. 2020), KITTI (Geiger et al.) 2013), and nuScenes (Caesar et al. 2020) datasets, finding that motion- level noise has the most significant impact on perception robustness. NLTE (Liu et al. 2022) highlighted the importance of interactions between image noise and multi- scale features for perception stability. Gupta et al. (2024) further showed that changes in weather conditions can also pose significant challenges object detection results. Additionally, works such as Song et al. (2024) et al. have jointly evaluated perception models in terms of accuracy, latency, and robustness, revealing that current methods still leave substantial room for improvement in safe and stable operation. Overall, perception robustness remains an unsolved challenge. Therefore, for reconstruction tasks, minimizing noise that could affect perception outcomes is crucial and necessary condition for practical deployment.
3 PRELIMINARY STUDY
3.1 PROBLEM DEFINITION
Given an input scene S. a 3DGS model R reconstruct a 3D representation of the scene. A percep- tion module(in this work, specifically detection model, since it is generally considered core and
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indispensable perception module in ADS systems (Gog al. 2021 Wang al. 2025 P produces necessary outputs for downstream modules ADS. In typical 3DGS tasks, the goal of reconstruc- tion is typically to minimize the difference between the ground truth image z E S and the rendered image the reconstructed model R(x). This objective can be expressed as
Crcon = ding (R(x),
(1)
where (+-) measures the visual quality(e.g. SSIM, PSNR).
However, the ADS testing task, the reconstruction should also keep the key information, especially the location of objects. To ensure the representation could be effectively used for ADS, the problem we address is to ensure perception stability: the outputs of the perception model P(.) applied to
the reconstructed model should remain consistent with those obtained from the ground truth scene, defined as
Epene dpere (P(R(x)), P(x))
where d'erel's-) measures the discrepancy at the semantic or perception level.
Formally, formulate the problem constrained optimization: the reconstruction quality must be sufficiently preserved, while minimizing the perception discrepancy.
min Es dep[ddeepPPP(@@ (P(R(x)), P(x))]
s.t. E ( x)] s,
3.2 PRELIMINARY STUDY
Based on the problem definition, would like first to explore the limitations of existing 3DGS methods in the traffic scene reconstruction task.
3.2.1 EXPERIMENTS SETUP
3DGS approaches We select three state-of-the-art 3DGS approaches: S (Huang et al, 2024). OmniRe (Chen et al. 2025). and EMD (Wei et al., 2025). In practice, EMD is built upon either S Gaussian or OmniRe as its base, and we conduct separate experiments for each base. Furthermore, all reconstruction training processes consist of 5,000 steps in the coarse stage, followed by 30,000 steps the fine stage.
Perception Model To avoid the influence of the perception model's architecture, we evalu- ate perception performance using three popular detection models with different architectures: YOLOv8 (Varghese & M. 2024), Faster R-CNN (Ren et al. 2015) and WEDDEERRhaa et al. 2024).
Metries As described, we aim to investigate the performance reconstruction methods terms of metries from both perspectives for compar- ison accordingly. We utilize SSIM to characterize and measure visual quality. Meanwhile, to main- tain perception stability, we mean loU to measure detection differences, and mAP [0.5:0.95] to quantify detection confidence and mis-classification. Additionally, we counted the number of missed detections after reconstruction.
both visual quality and perception stability, and
Ground truth It should be emphasized that the calculation of the perception stability part does not directly compare against the object information in the ground truth; instead, is based on the outputs of the same perception model when fed with the ground truth.
Experiment scenes We fully reproduced all experimental scenes of 3 GaGussiaa and OmniRe. For the EMD method, experiments were conducted using the scenes corresponding to each respective base method. To limit space, all results reported in the paper are averages across all scenes.
3.2.2 PRILMINARY RESULTS
Table shows that existing 3DGS methods generally achieve high visual quality. It can be observed that existing methods already exhibit high performance in visual metrics whether in SSIM, PSNR, or LPIPS- but their perception stability is not satisfactory, yet they are fair to use.
To better understand this mismatch, we further examined the statistical relationship between pixel- Although these visual metrics do show
level metrics and perception stability, shown as table 2
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Table Visual Quality and Perception Metrics of Existing Methods (On Average)
pixel Level
YOLO v8
Faster RCNN
SSIM
PSNR
LPIPS
mAP
mean IOU
Miss
mAP
mean IOU
Miss
Gaussian 0.924
31.27
0.105
0.550
0.803
1.5
0.171
0.620
2.0
OeniRe 0,953
33.77
0.049
0.489
0.832
0.0
0.320
0.718
0,3
GMD(S*G) 0,923
31.39
0.104
0.578
0.755
0.0
0.270
0.689
0.5
EMD(OmniRe)
0.962
35.02
0.039
0.452
0.839
0.3
0.348
0.768
0.3
Table 2: The statistical correlation between pixel-Ievel metrics(mAP)) and detection stability with Yolo v8. Limited to page size, more data(correlations with meanlOU) will be shown in the Ap- pendix. denotes the Pearson correlation coefficient, and p denotes the p-value.
Correlation
TSSIM
PSSIM
TPSNR
PPSNR
TLPIPS
PLPIPS
Gaussiia
0.767
2.43E-3
0.658
3.09E-3
-0.721
1.21E-3
OmniRe
0.417
3.11E-3
0.484
1.07B-3
-0.568
2.50E-3
EMD(S*G)
0.374
2.14E-3
0.319
1.09E-3
-0.387
2.33E-3
EMD(Omnikee)
0.295
1,41E-3
0.439
1.52E-4
-0.369
3.76E-3
statistically significant correlations with mAP and mean IoU (all p < 5E --8--,,, their Pearson coef- ficients remain only in the range of 0.3-0.6. This indicates that the connection between the two is fairly weak: visual improvements tend to move in the same direction as perception stability, but the effect is limited and far from predictive.
4 METHOD 1: PERCEPTION-ALIGNE LOSS INTEGRATION
As we found in Section 3.2 in the ADS traffic scene generation task, existing 3DGS methods could achieve high visual quality, but often fail guarantee stable perception outputs for objects. To ad- dress this limitation, we first propose naive yet intuitive solution that integrates detection results as an additional loss term directly into the 3DGS model training process. This approach would en- courage the training process not only to focus on visual quality but also to preserve stable perception results.
Formally, define the perception-aligned loss Spere as the sum of the errors in the predicted bound- ing boxes and their associated class labels as
Lpere- Σ (Abox Lrox (B(x), B(R())) + Ads Lex (C(E)))
(4)
Perception Stability Assessment
Design Perception Stability
Perception stability
Perception-aligned Loss
Detection office
Complete LOU
Mis ciscoiiiiictoon
Dataset
truth
Perception Model
Detection mines
Object Quality
Incorrect class
Ocject zome mask
Quality measure
Conficence
3DGS
Reconstructed
Figure 2: Overview of this work. Perception stability measured by comparing the outputs of the same perception model when fed with the original frames versus the reconstructed frames. Based on the perception outputs and the object regions identified by the perception model, we designed perceptioo-aalined loss and an object zone quality loss to improve perception stability.
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where B,C P refers to the bounding box and classification label in the perception model results, respectively, and À is the weight of corresponding items.
In autonomous driving tasks, object bounding boxes not only require sufficient overlap with the ground truth but also accurate center positions, as these factors greatly affect subsequent modules such tracking and prediction (Yin al. 2021 [Sharath & Mehran 2021). Meanwhile, the aspect ratio of the bounding box also affects the ADS's s classification of the target type and can influence subsequent decision-making (Luo et al. (022). CCom(Com IoU) (Zheng et al. (022)) naturally satisfies both of these requirements by jointly penalizing deviations in overlap, center-point distance, and aspect ratio, making particularly suitable as a loss function for bounding box regression in ADS scenarios. And the perception alligned loss is defined as
Lox
1
(B))
(5)
n
where B; is the the bounding box of the i-th object in a given frame and n is the total number of objects in the frame.
On the other hand, the classification loss is computed directly based on whether the predicted class labels are the same in the ground truth frame and the reconstruction frame,
Lels = 1 E -CC()))CC((()))))
(6)
Finally, this perception-aligned loss is integrated into the total training objective
Liote!
Avisual
-visual + Aperc
Lpere
where Lvisual is the existing visual quality-aware loss function. Note that this loss is only backprop-
agated during 3DGS training, while the perception model remains frozen and not updated any way.
EXPERIMENTS FOR METHOD
5.1 EXPERIMENTS DESIGN
To evaluate the effect of integrating perception-aware losses into the 3DGS training, YOLOv8 as the perception model. We first apply YOLOv8 to the frames of the training data to obtain P(x). During the 3DGS training process, the same YOLOv8 is then applied to each recon- structed frame to extract P(R(x)). The perception-aligned loss is computed according to equations and and integrated into the 3DGS training objective in equation guiding the reconstruction to preserve stable perception outputs. Similar to existing work, this loss will be applied only in the fine-stage training.
To simplify the choice of parameters and to focus on the effect of the perception-aligner loss, we set all À values (Abox, Adts Avisual Apere) 1. This allows us to clearly see how the perception-alignerr loss affects the reconstruction, without the results being influenced by different weighting factors.
All comparison methods, scenes, and metric selections are kept the same as Section 3.2.1
5.2 EXPERIMENT RESULTS
Table shows the results of perception-aligned loss integrated into the training process. We can draw the following conclusions: /) Integrating the aaaeietio-aligned loss leads to a significant improvement mAP and mean IoU, indicating that the reconstructed scenes achieve stronger per- ception stability; 2) The same trend is observed on Faster R-CNN and RT-DETR, in addition to the YOLOv8 used during training, suggesting that the improved perception stability is not limited to the training model but indeed reflects an enhancement in reconstruction quality; 3) The visual quality loss exhibits only minor fluctuations (# < 1% ), indicating that adding the perception-aligee aligned loss has relatively small and acceptable impact visual quality.

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Table 3: Perception-aliggee Loss Integration, use YOLOv8 guidance model, test Faster RCNN and RT-DETR as black-box model
Pixel-Level
YOLOv8
SSIM
PSNR1
LPIPS
mAPt
mean IoUf
Miss.
Gaussian
0.924
31.27
0.106
0.550
0.803
1.5
S G
0.920
31.53
0.106
0.593
0.840
0.83
OmniRe
0,953
33.77
0.049
0,489
0.832
0.0
OmniRe+Cper
0.954
33.75
0.048
0.507
0.845
0.0
EMD(S
0,923
32.89
0.057
0.578
0.755
0.0
EMD( Cpare
0,951
33.37
0.046
0.583
0.857
0.0
EMD(OmniRe)
0,962
35.02
0.039
0.452
0.839
0.3
EmD(Ommniee+&Coo
0.954
35.42
0.035
0.497
0.846
0.0
Faster R-CNN
RT-DETR
mAP
mean IoU
Miss.
mAPf
mean IoUf
Miss.
S
Gaussian
0.171
0.620
2.0
0.494
0.829
0.0
S Gaussian+
0.229
0.632
0.509
0.829
0.0
OmniRe 0.320
0.718
0.3
0.519
0.789
0.0
OmniRe+CCuen
0.360
0.722
0.0
0.526
0.792
0.0
EMD(S
0.270
0.689
0.5
0.518
0.770
0.0
EMD(S*))+L
0.307
0.701
0.0
0.674
0.875
0.0
EMD(OmmiRR))
0.348
0.768
0.3
0.542
0.852
0.0
EMD(OmmiiR))+Ceeer
0.352
0.779
0.0
0.542
0.842
0.0
METHOD 2: OBJECT ZONE QUALITY LOSs FOR RECONSTRUCTION
6.1 REVIEW OF PERCEPTION-ALICNN LOSS INTEGRATION
In the experiments presented in Section we have demonstrated the positive effect the perception-aligned loss on perception stability. However, this approach still has two limitations: /) the improvement perception is derived from the perception model's outputs, which limits inter- pretability; 2) during training, inference through the perception model is required at every iteration, significantly increasing training time and memory consumption. Therefore, we proceed to further analysis based on the existing results.
Through a study of numerous cases where the perception model is affected after reconstruction, we observed two typical phenomena, shown in Figure 3 The first, illustrated in I, shows disconti- nuities in object regions, which mostly occur in static objects. The second, shown in II, exhibits blurring in object regions, and this is most pronounced in dynamic objects. Both issues point to common problem: current 3DGS methods for traffic scene reconstruction tend to produce a larger shortfall in object regions compared other layers, such as the sky buildings. Previous studies have shown that, due to its reliance on LiDAR or other point cloud data, 3DGS often lacks fine- grained reconstruction object edges (Chelani al.) 2025 Lu al.) 2025), resulting in blurred object edges and perceptual changes. Furthermore, in most scenes, object regions occupy much smaller area than other layers. Under existing methods, the reconstruction quality of object regions is even less reliably guaranteed.
6.2 OBJECT ZONE QUALITY LOSS
Based on the analysis in Section 6.1 we believe that core reason for the instability perception after reconstruction the relatively low reconstruction quality of object regions. Thus, propose

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(I-a) ground truth
(I-b) S Gaussian
(I-c) S Gaussian+ Spore
(II-a) ground truth
(II-b) S Gaussian
(II-c) 5 S*aussian
Figure 3: Comparison of reconstructed for Gaussian, the perception model fails to maintain orig- inal outputs, whereas integrating the perception-alignel loss leads to improved perception consis- tency. I: modelling fractures; II: object zone blur
more intuitive solution: we design custom object zone quality loss that computes a separate visual quality loss for the object regions identified by the perception model.
Loij = divis (R(x) @ B(x), x O B(x))
where applies the mask to extract the object zones, and dyis is visual similarity metric. Then, similar to perceptiio-allineed loss integration, the object zone quality loss is utilized as
Listal
Avisual
Lvisual +
Xobj-vis
Lobjovis
By introducing the proposed object-zone quality loss, the reconstruction process is clearly guided to emphasize regions that the perception model deems critical in the ground-truth frames (i.e.. the de- tection zone). This targeted enhancement improves the fidelity of object regions, thereby improving perception stability. Furthermore, since the training procedure relies solely offline ground-truth perception results, this approach offers improved computational efficiency compared to Method 1, which requires direct incorporation of parception-alinned loss for each iteration.
7
EXPERIMENTS FOR METHOD 2
7.1 EXPERIMENT DESIGN
When evaluating reconstruction quality, this experiment introduces metric, the object loss, which measures reconstruction quality specifically within the object regions identified by the perception model in the ground-truth images. addition. mentioned in Section 6.2 the object zone quality loss reduces computational complexity compared to the perception- aaligned loss. We also record and compare the time cost during the reconstruction training process quantify this efficiency difference.
7.2 EXPERIMENT RESULTS
Table shows the results of object zone quality loss integrated into the training process. The exper- imental results indicate that:
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Table Object Zone Quality Loss Integration, use YOLOv8 as guidance model, test Faster RCNN and RT-DETR as black-box model
Pixel-Level Obj SSIM
YOLO vå
SSIM
PSNR
LPIPS..
mAPT
mean 1OU1
Miss.
Gaussian
0.924
0,877
31.27
0.106
0,550
0.803
1.5
Gaussiant Epere
0.920
0.897
31.53
0.106
0.593
0.840
0.83
Gaussian+ Lobj-vis
0.937
0,921
31.89
0.082
0.672
0.862
0.4
S Gaussian+ Cepeec Eepjsct Lobi-riis
0.941
0.924
32.00
0.083
0.700
0.872
0.0
OmniRe
0.953
0.867
33.77
0.049
0.489
0.832
OmmiRe+ Epene
0,954
0,876
33.75
0.048
0.507
0.845
OmniRe+ Cabjovi
0.949
0.893
33.25
0.046
0.545
0.856
OmniRe+ Spec
0.957
0.899
33.75
0.047
0.609
0,870
0.0
EMD(S GG)
0.923
0.855
32.89
0.057
0.578
0.755
0.0
EMD RMD(S*))+ G)
0.951
0.923
33.37
0.046
0.857
EMD(S G)+ votj-vis
0.952
0,946
33.56
0.043
0.601
0.843
0.0
EMD ()) Cpecc Cobjovis
0.952
0.948
33.49
0,043
0.600
0,860
0.0
EMD(OumiRe)
0.962
0.910
35.02
0.039
0.452
0.839
EMD(OnumiRe++Com Crere
0.954
0.915
35.42
0.035
0.846
EMD(OmmiRR)+ Cobj-vis
0.965
0,942
35.20
0.034
0,508
0.856
BMD(OOmniRe)+CCr Libj-sis
0.969
0.940
35.33
0.035
0,510
0.856
0.0
Faster RCNN mean IOU1
RT-DETR
MAP
Miss
MAPt
mean IOU1
Miss.
S Gaussian
0.171
0.620
2.0
0.494
0,829
0.0
5 Gaussian + perr
0,229
0.632
0.7
0.509
0.829
0.0
5 Gaussian Lobj
0.271
0.689
0.4
0.603
0.843
0.0
S Gaussian S L
0.269
0.695
0.3
0.610
0.845
0.0
OmniRe
0.320
0.718
0.3
0.519
0.789
0.0
OmniRe+ Lpere
0.360
0.722
0.0
0.526
0.792
0.0
OmniR Lobj-vis
0.343
0.735
0.0
0.524
0,800
0.0
OmniR c Cenc C
0.359
0.740
0.0
0.675
0.874
0.0
0.270
0.689
0.5
0.518
0.770
0.0
EMD(5 (MD(SG))++4 +)
0.308
0.715
0.1
0.674
0.875
0.0
EMD(S ( G)+ Lobj-vis
0.308
0.701
0.0
0.666
0,870
0.0
EM
G)+ 9
penc Lobj.vis
0.311
0.717
0.0
0.677
0.879
0.0
EMD(OmniRe)
0.348
0.768
0.3
0.542
0.852
0.0
(MD(OOmniRe)&.4 Lypee
0.352
0.779
0.0
0.542
0.842
0.0
EMD(OmmiiRe) Lobj
0.389
0.778
0.0
0.558
0.862
0.0
EMD(OmmiRe)) Coo Cobj-vvs
0.404
0.783
0.0
0.562
0.879
0.0
The application of the object zone quality loss explicitly emphasizes these regions training focus, leading to a significant improvement in the reconstruction quality of the targeted areas. At the same time, this also has positive effect the overall visual quality. The perception stability the reconstructed scenes has been significantly improved, which also implies enhancement in the reconstruction quality of object regions, with more detailed information such as edges and textures being preserved. This, in turn, benefits the recognition performance of perception models.
Compared to the naive approach that directly employs a perception-aligee zone quality loss provides more stable improvements in reconstruction quality, However, in specific scenarios, the IoU accuracy does not necessarily outperform that of the perception- aligned loss.
loss, the object
Optimizing with both losses simultaneously can, in most cases, yield better results com- pared to using either loss individually, although the improvement relatively modest.

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Published as a conference paper at ICLR 2026
7.3
RUNTIME ANALYSIS
Table 5: Average reconstruction training process time consumption.
per 100 epochs(seconds)
in total (minutes)
origin
with Lprr
with Cobj-vis
origin
with Lpore
with Cobj-vis
S3G
25.20
26.67
25.30
204.4
232.2
205.4
OmniRe
34.84
36.07
34.91
282.1
332.2
283.5
EMD(S3G)
32.44
33.94
32.51
262.9
312.9
263.9
EMD(OmniRe)
44.94
46.45
45.00
364.5
413.2
364.9
As shown in the Figure 5
incorporating perception loss into the training process requires an ad- noticeable increase in training time. In
ditional YOLO inference at each iteration, leading introducing an extra loss computation, has contrast, the use of object zone quality loss, although only a negligible impact on overall runtime. Note that: /) these results are obtained on our exper- imental platform with an RTX A5000 GPU, and the absolute values may vary depending on the computing device; 2) In our experiments, we employ the lightweight YOLOv8n model; using a heavier detector, such Faster R-CNN, would likely exacerbate this difference.
DISCUSSION
Loss Weight and Trade-offs: In our experiments, all loss weights (A) were set to to demonstrate the effectiveness of the proposed methods to avoid the effect of parameter selection. However, this work does not fully explore the trade-off between maintaining high visual realism and ensuring stable perception outputs by adjusting the loss weights. For example, in our task, the scenes contain many objects, but each occupies a relatively small area. In contrast, robotic manipulation tasks often involve key targets within the robot's field of view that occupy larger portion of the scene, but typically few in number. Clearly, these two scenarios would require different combinations of values, a topic not explored in this work. Future work could study adaptive or learned weighting strategies balance this trade-off more effectively.
While this work focuses on traffic scene reconstruction for autonomous driving systems, the principle of perception-aware reconstruction may not be limited to this do- main. Any modular AI system that relies on perception outputs for downstream tasks-such as robotics, AR/VR, or automated inspection systems-coule potentially benefit from our approach. For example, in robotic manipulation, an unstable perception of object positions could result in in- correct grasps or collisions; AR/VR, inconsistent perception could disrupt object alignment and user experience; in automated inspection systems, unstable detection could lead to missed defects. Our approach enhances reliability and trustworthiness reconstructed scenes across these domains. From the current perspective, our method does not rely on strong domain-specific knowledge. In principle, could easily adapted to these perception-critice tasks.
Broader Applicability:
CONCLUSION
We introduced perception-aware reconstruction, aiming to ensure 3DGS preserve both visual quality and perception stability in ADS traffic scene reconstruction. Through our experiments, we found that existing methods, while improving visual quality, do not guarantee an increase in percep- prancchss-prcepptiooo--o
tion stability. To address this, propose two quality loss to effectively improve perception stability across multiple 3DGS methods and detec- tion models. Our results demonstrate that both approaches significantly enhance the perception stability of reconstructed scenes. This work points the way toward 3D reconstructions that are both realistic and practical for safety-critical applications.
loss and object zone
10

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Published as a conference paper at ICLR 2026
10 ACKNOWLEDGEMENT
This work was supported in part by JST CRONOS Grant (No. JPMICS24K8), JSPS KAKENHI Grant 2No.J221004877, No.JP23H3337, and No.JP24K02920), Canada CIFAR AI Chairs Pro- gram, the Natural Sciences and Engineering Research Council of Canada.
A REPRODUCIBILITY STATEMENT
A.1 RECONSTRUCTED SCENES
Our experiments are entirely based on the Waymo dataset, using the same seene selection as in S Gaussian and EMD.
For the experiments of 5 Gaussian and E Gaussian), we use scene ids: 003, 019, 021, 022, 036, 069, 081, 094, 126, 139, 140, 146, 148, 157, 181, 200, 204, 237, 241, 297, 302, 314, 362, 427, 482, 495, 524, 527, 581, 700, 753, 780, 795
For the experiments of OmniRe and EMD((OmniRe), we use scene ids: 014, 016, 021, 022, 023, 031, 049, 053, 064, 080, 088, 094, 111, 114, 222, 327, 552, 621, 700, 784, 785, 788, 796
A.2 EXPERIMENTAL ENVIRONMENT
We conduct our experiments primarily on a server with an Intel 10920X and dual NVIDIA RTX A5000 GPUs. In practice, most experiments can be completed with 24GB of VRAM. Few scenes (like 788) would take more VRAM, and we deploy these scene experiments on server with an NVIDIA V100 with 32 GB of VRAM.
A.3 IMPLEMENTATION DETAILS
Here are some implementation details not mentioned in the main text.
Perception model weights
YOLO: yolov8n from ultralytics;
Faster RCNN: fasterrenn reereet5..p from torchvision
Image load size The image load size of the four base approaches is not the same, and we set it to [320, 480] for each camera.
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Reviews:
[{"review_id": "EIuOfieDWa", "reviewer": "Reviewer_ei8B", "summary": "This paper points out that existing 3D Gaussian Splatting methods focus on global visual fidelity, which does not necessarily preserve the perception outputs of autonomous-driving detectors. To address this, the authors introduce two simple training objectives—a perception-aligned loss and an object-zone quality loss—that encourage consistency between reconstructed and original images at the detector level. Experiments on Waymo scenes show improved detection consistency (mAP/IoU/Miss) over several 3DGS baselines without degrading visual quality.", "questions": "Please clarify the gradient pathway in the perception-aligned loss: is NMS bypassed, and how are matched detections determined when counts differ?\n\nTo substantiate the “visual ≠ perception” claim, report correlations among SSIM, PSNR, LPIPS, and mAP, or plot them across scenes.\n\nAdd an efficiency table (e.g., training time per 1k frames or per epoch) comparing baseline 3DGS, + L_perc, and + L_obj.\n\nConsider a lightweight feature-alignment proxy (e.g., backbone-feature L2/KL) to avoid full detector forward passes.\n\nIf possible, include a simple downstream experiment  to show whether perception-stable reconstructions yield tangible ADS gains.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The insight that “high SSIM ≠ stable perception” is novel and meaningful. Bridging 3DGS reconstruction and detector consistency provides a new angle on how generative scene reconstructions might better serve ADS evaluation.\n\n2.  Despite the conceptual simplicity, the proposed objectives yield measurable mAP/IoU gains across multiple 3DGS baselines and even transfer to unseen detector architectures, indicating that the effect is real and not detector-specific.\n\n3. The paper is concise, well-structured, and easy to follow; figures and tables are clear, and the motivation flows logically from observation → method → experiment.", "weaknesses": "1. Limited evidence for “visual-quality ≠ perception.” The claim is mainly supported by SSIM vs mAP differences in one table. Including PSNR, LPIPS, or correlation plots would make the observation statistically more convincing.\n\n2. Running a full YOLO inference at every iteration introduces heavy overhead. While the authors mention this qualitatively, no quantitative efficiency table or training-time comparison is given.\n\n3. The work evaluates only object detection, whereas ADS perception also involves segmentation, tracking, and planning. Without any closed-loop or end-to-end evidence, it remains unclear how perception-stability improvements benefit actual ADS behaviour.\n\n4. The perception-aligned loss is computed after a detector that includes NMS and hard thresholding, which are non-differentiable. The paper does not explain how gradients flow through this step or whether only pre-NMS continuous outputs are used. As written, the formulation is theoretically questionable.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "H6oSyn9VIn", "reviewer": "Reviewer_45Bt", "summary": "This paper identifies that existing 3D Gaussian Splatting (3DGS) methods for autonomous driving focus on visual quality but neglect the generation performance regarding to downstream perception task.. To address this, the authors propose two simple yet effective losses: (1) Perception-aligned loss, aligning reconstruction with detection results, and (2) Object-zone quality loss, emphasizing reconstruction quality on detected object regions. Experiments on Waymo scenes using S3Gaussian, OmniRe, and EMD show improved mAP, mean IoU, and fewer missed detections, while maintaining similar SSIM/PSNR.", "questions": "As discussed in the weakness:\n\n+ Generalization:\nHow does $\\mathcal{L}_{\\text{obj-vis}}$ perform when trained with one detector (e.g., YOLO) but evaluated using a fundamentally different model family (e.g., DETR-based architectures or 3D detectors)?\n\n+ Task Scope:\nHave you explored extending the framework to other perception tasks—for example, leveraging semantic segmentation masks to emphasize safety-critical pixels? Additionally, does the proposed approach improve temporal stability or downstream planning performance in autonomous driving systems?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Important and Well-Motivated Problem: The paper clearly identifies a fundamental limitation in current 3D reconstruction pipelines for autonomous driving simulation. The proposed concept of perception stability offers a more meaningful and task-relevant objective than traditional visual fidelity metrics.\n\nSimple, Efficient, and Effective Solution: The proposed loss term, $\\mathcal{L}_{\\text{obj-vis}}$, is intuitive, easy to integrate, and incurs minimal computational overhead, yet consistently yields substantial gains in perception stability and reconstruction quality.", "weaknesses": "+ Model-Specific Limitation:\nThe framework’s dependence on a single “ground-truth” perception model ($\\mathcal{P}$) may restrict the generality of the reconstructed scenes, potentially reducing their usefulness for training or evaluating diverse perception architectures.\n\n+ Limited Perception Scope:\nThe study defines “perception” solely in terms of 2D object detection, overlooking other essential autonomous driving tasks such as 3D detection, semantic segmentation, and multi-object tracking.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0u3XnVrVl2", "reviewer": "Reviewer_sxkk", "summary": "This paper points out that existing 3D Gaussian Splatting (3DGS) methods for street-scene reconstruction overlook the essential requirement of preserving autonomous driving system (ADS) behavior. The authors argue that optimizing solely for visual reconstruction performance fails to ensure consistency in downstream perception modules. To enhance the reconstruction quality of object instances, the paper introduces two complementary losses: Perception-Aligned Loss and Object-Zone Quality Loss. Experimental results demonstrate the effectiveness of the proposed approach in improving perception consistency without compromising visual fidelity.", "questions": "1. Would an adaptive λ scheduling strategy (balancing Lvisual vs Lperc/Lobj) further improve trade-offs?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. The paper is well-motivated, making its ideas easy to follow.\n2. Simple yet effective methods: perception-aligned and object-zone losses are easy to implement and show consistent gains.\n3. Comprehensive evaluation: multiple base 3DGS models, perception backbones, and visual/perceptual metrics are tested.", "weaknesses": "1. The writing and figures need to be refined, especially in the Introduction and Method section. The figures contain text that is too small to read clearly and would benefit from clearer visual design and labeling.\n2. The Broader Applicability discussion provides limited new insights. The paper would be stronger if it included a deeper analysis or ablation of how the proposed methods generalize across different tasks or domains.\n3. The performance improvement appears somewhat incremental. Evaluating the method on additional and more diverse scenarios could better demonstrate its effectiveness and practical significance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8eVzPHpnKB", "reviewer": "Reviewer_7ST4", "summary": "This paper identifies a gap in 3D Gaussian Splatting for autonomous driving: existing methods optimize global visual similarity, but this does not ensure consistent perception outputs, which are crucial for ADS behavior. It proposes perception-aware reconstruction with two losses: a perception-aligned loss penalizing output inconsistencies, and an object-zone quality loss emphasizing task-relevant regions. Experiments show improved perception stability while maintaining visual fidelity.", "questions": "To clearly demonstrate the effect of each component, a combination of Object Zone Quality Loss and Perception-Aligned Loss integration should be evaluated in Table 3.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper presents novel evaluation metrics that specifically assess perception performance, which is meaningful for autonomous driving simulation.", "weaknesses": "1. One weakness is that the results appear to be highly dependent on the chosen perception model. While testing with Faster R-CNN, more baseline methods should be included for comparison.\n\n2. As shown in Table 2, the Perception-Aligned Loss leads to a drop in SSIM for some methods. While the idea of using perception scores is meaningful, the trade-off between visual quality and perception alignment is not clearly analyzed.\n\n3. The Object Zone metric is fairly straightforward and not novel, as similar concepts such as Dynamic Region PSNR have been used in prior works.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)