DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving
Authors: Hao LU
Tianshuo Xu
Wenzhao Zheng
Yunpeng Zhang
Wei Zhan
Dalong Du
Masayoshi Tomizuka
Kurt Keutzer
Ying-Cong Chen
Venue: NeurIPS 2025 poster ()
Keywords: Autonomous Driving;  4D Gaussian Reconstruction
Primary area: deep_learning

Abstract: Large reconstruction model has remarkable progress, which can directly predict 3D or 4D representations for unseen scenes and objects. However, current work has not systematically explored the potential of large reconstruction models in the field of autonomous driving. To achieve this, we introduce the Large 4D Gaussian Reconstruction Model (DrivingRecon). With an elaborate and simple framework design, it not only ensures efficient and high-quality reconstruction, but also provides potential for downstream tasks. There are two core contributions: firstly, the Prune and Dilate Block (PD-Block) is proposed to prune redundant and overlapping Gaussian points and dilate Gaussian points for complex objects. Then, dynamic and static decoupling is tailored to better learn the temporary-consistent geometry across different time. Experimental results demonstrate that DrivingRecon significantly improves scene reconstruction quality compared to existing methods. Furthermore, we explore applications of DrivingRecon in model pre-training, vehicle type adaptation, and scene editing. Our code will be available.

SeivingRecon, Large 4D Gaussian Reconstruction Model For Autonomous Driving
Hao LU
Tianshuo XU Wenzhao ZHENG`, Yunpeng ZHANG*, Wei ZHAN3, Dalong DU Masayoshi Tomizuka", Kurt Keutzer", Yingcong CHEN 'The Hong Kong University of Science & Technology (Guangzhou),
"The Hong Kong University of Science & Technology. University of California, Berkeley, 4PhiGent Robotics
Mati-Vion Imago
Mali-Vin Image
Large SunneSSpaaa--emmmrra Tempurary (Foed-Forward Model)
Potcatial Application
Pre-training Model
Adaptation
Figure 1: Leveraging temporal multi-view images, the Large 4D Gaussian Reconstruction Model (DrivingRecon) can predict 4D driving scenes. DeivingRecon serves as a pre-trained model that effectively captures geometric and motion information. Additionally, DrivingRecon can synthesize novel views based on specific camera parameters, ensuring adaptability to various vehicle models. DrivingRecon further facilitates editing of designated 4D scenes.
Abstract
Large reconstruction model has remarkable progress, which directly predict 3D or 4D representations for unseen scenes and objects. However, current work has not systematically explored the potential of large reconstruction models in the field of autonomous driving. To achieve this, we introduce the Large 4D Gaussian Reconstruction Model (Drivinggeconn) With elaborate and simple framework design, it not only ensures efficient and high-quality reconstruction, but also provides potential for downstream tasks. There are two core contributions: firstly, the Prune and Dilate Block (PD-Block) is proposed to prune redundant and overlapping Gaussian points and dilate Gaussian points for complex objects. Then, dynamic and static decoupling is tailored to better learn the temporary- consistent geometry across different time. Experimental results demonstrate that DrivingRecon significantly improves scene reconstruction quality compared to existing methods. Furthermore, we explore applications of DrivingRecon model pre-training, vehicle type adaptation, and scene editing. Our code is available at DriveRecon
WWok done while visiting UC Berkeley. Cooresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Introduction
Autonomous driving has made remarkable advancements in recent years, particularly in the areas of
perception [25] 76, 21 58, [5], prediction [17 [1 26 7161,, and planning [10 g 9 19
73
72 33)
With the emergence of end-to-end autonomous driving systems that directly derive control signals from sensor data [88 19 22), conventional open-loop evaluations have become less effective [74).
Real-world closed-loop evaluations offer promising solution, where the key lies in the development
of high-quality scene reconstruction [50 62
30
29
31
27
28
39
69
34]..
Despite numerous advances in photorealistic reconstruction of small-scale scenes 37
38
23
57
34]. modeling large-scale and dynamic driving environments remains challenging. Most existing methods tackle these challenges by using 31) bounding boxes to differentiate static from dynamic
components [64] 60 [5)_ Subsequent methods learn the dynamics in a self-supervised manner
with a 4D NeRF 666] or 3D Gaussian [20) The aforementioned methods require numerous and
time-consuming iterations for reconstruction and cannot generalize to new scenes.
Some recent methods are capable of reconstructing 3D objects 16 75 or 3D indoor scenes 46
in a forward way. Many forward approaches have also been tried in the autonomous driving 448 42 65 56 (5). However. all feed-forward methods predict a Gaussian point for each pixel, and the pixels from multi-view images are fused together. This paradigm has the following disadvantages: (1) The multi-vies vielw images have overlaps, and the Gaussian points corresponding to the overlapping regions will cause artifact. (2) Complex objects require more Gaussian points to represent, and the fixed number of predicted Gaussian points per region limits the quality of the image. Besides, only same moment images are used to supervise the rendered dynamic object, which limits the geometric and apparent quality of the dynamic object.
To this end, we introduce a Large Spatial-Tempon Reconstruction Model (DrivingRecon) for autonomous driving. Our framework uses the shared 2D encoder and range-view decoder, which ensures that the image encoder can serve multiple autonomous downstream tasks. The most important innovation to solve pain points is that the decoder is consisting of Prune and Dilate Blocks (PD- Blocks). The PD-Block effectively prunes overlapping Gaussian points between adjacent views and redundant background points. The pruned Gaussian points can be replaced by dilated Gaussian points of complex object. Finally, static and dynamic decoupling is proposed to improve the quality of dynamic objects. The experimental results show that our method is significantly improved compared with the existing algorithms. Our main contributions are as follows:
We propose the PD-Block, which learns to prune redundant Gaussian points from different views and background regions. It also learns to dilate Gaussian points for complex objects, enhancing the quality of reconstruction.
We design rendering strategies for both static and dynamic components, allowing rendered images to be efficiently supervised across temporal sequences.
We explore the effectiveness of DrivingRecon in reconstruction, pre-training, vehicle adap- tation, and scene editing tasks.
2 Related Work
2.1 Driving Scene Reconstruction
Numerous efforts have been put into reconstructing scenes from autonomous driving data captured in real scenes. Existing self-driving simulation engines such as CARLA 12) or AirSim 43] suffer from costly manual effort create virtual environments and the lack of realism in the generated data. Many studies have investigated the application of these methods for reconstructing street scenes. Block-NeRF [70] and Mega-NeRF [49] propose segmenting scenes into distinct blocks for individual modeling. Urban Radiance Field 40 enhances NeRF training with geometric information
from LIDAR, while DNMP [2) utilizes a pre-trained deformable mesh primitive to represent the scene. Streetsurf [15] divides scenes into close-range, distant-view, and sky categories, yielding superior reconstruction results for urban street surfaces. MARS 600 employs separate networks for modeling background and vehicles, establishing an instance-aware simulation framework. With the introduction of 3DGS 23), DrivingGaussian (77] introduces Composite Dynamic Gaussian Graphs and incremental static Gaussians, while StreetGaussian [64] optimizes the tracked pose of dynamic

---

Mult-Pie Imagie
Farture
Range-//an Decoder
rolaece
segnirraioon
Image Encoder
Image Fartures
(b)
Dynamic Rendering
Inteiees
The overview of DrivingRecon. (a) Framework: multi-view images are in turn sent to the shared image encoder, and range view decoder to directly predict 4D Gaussians. (b) For dynamic objects, we only use next time-step images to supervise the current Gaussian parameters. For static scenes, rendering supervision is used across timestamps. In addition, reconstruction loss is also applied.
Figure 2:
Gaussians and introduces 4D spherical harmonics for varying vehicle appearances across frames. Omnire 7 further focus on the modeling non-rigid objects driving scenarios. However, these reconstruction algorithms requires time-consuming iterations to build new scene.
2.2 Large Reconstruction Models
Some works have proposed to greatly speed this up by training neural networks directly learn the full reconstruction task a way that generalizes to novel scenes (70, 52 51 59]. Recently, LRM [16] among the first utilize large-scale multiview datasets including Objaverse [II] to train a transformer based model for NeRF reconstruction. The resulting model exhibits better generalization and higher quality reconstruction of object-centric 3D shapes from sparse posed images a single model forward pass. Similar works have investigated changing the representation Gaussian splatting [4 65]. introducing architectural changes support higher resolution [3] 44
and extending the approach to 3D scenes 12. 6). Recently, L4GM utilize temporal cross attention fuses multiple frame information predict the Gaussian representation of dynamic object [4].
Many feed-forward approaches have also been tried in the autonomous driving 48 42
65 56
However, all feed-forward methods predict Gaussian point for each pixel. This paradigm will cause artifacts in the overlapping areas of multiple view.
3 Method
In this section, we present the Large 4D Reconstruction Model (DrivingRecon), which generates 4D scenes from surround-view video inputs in a single feed-forward pass. Section 3.1 details the overview of DrivingRecon. In Section 3.2, we provide an in-depth examination of the Prune and Dilate Block (PD-Block). Finally, Section 3.3 discusses our training, which includes static and dynamic decoupling strategy.
3.1 Overall framework
Problem Definition. DrivingRecon utilizes temporal multi-view images D train a feed-forward model G f(D,E, E, R R. V). For the i-th sample, D = {X' K",E" = 1..... T} includes N multi-view images X [1, ......... I,.... ... IN) at each timestep I, with corresponding intrinsic parameters gº [EI.... Ej. extrinsic translation y (V,..., Vj,.., V. The extrinsic parameter to project the camera coordinate system directly into the world coordinate system. We take the video start frame as the
E extrinsic rotation R$ (Ri,..., R2 ..... Rx }. and

---

Prune
Vult-Wiw Peatures
Concoting
(1) Redundancy
Denaity Assppike Foctures
(a) Repetition of Different Views
(b) Redundant Background
(c) Prune and Dilate Block
Figure 3: The motivation and details Prune and Dilate Block (PD-Block). (a) Different views predict repeated Gaussian points, causing the model collapse. (b) Simple backgrounds (blue dots) do not need a large number of Gaussian dots to be represented, while complex objects (red dots) need more Gaussian dots to be represented. (c) PD-Block fuse the multi-viww image features into range view form. Then PD-Block prune and dilate the Gaussian points according to the complexity of the scene.
origin of the world coordinate system. This ferw-forward model predicts Gaussians g the structure of (xyz E R$, rgb RR3 IR E R$, c E [R|C| R², Axyz R Ar E R.). These elements represent position, RGB color, scale, rotation vectors, semantic logits, position change and rotation change, respectively.
(G E Rd in
Motivation. DriveRecon uses the shared 2D encoder and range-view decoder. The framework was designed with two principles in mind: (1) Pre-training, Driving tasks such as end-to-end and object detection typically use a shared image encoder 25 76
53 22]. For 4D reconstruction tasks, the shared image encoder learn geometry, motion, and appearance information. (2) Efficient fusion. Unlike perception and planning tasks, 4D reconstruction requires decoding high resolution features. The range-viee decoder splice adjacent image features together, so that features with similar
space can be efficiently fused [24]] 13
Pipeline. Specifically, the temporal multi-view images D) are processed through a shared image encoder Fing to extract image features cing as shown in Fig. 2 The 3D position code is stitched into the channel of the image. Adjacent image features concatenated together in the range view form. Then, different time features are stitched together in different channels. These features are fed into the range-view decoder. The range-view decoder mades up of the Prune and Dilate block (PD-Block). Finally, the Gaussian adapter in range-view decoder transforms the decoded features into Gaussian points.
3.2 Learn to Prune and Dilate
The feed-forward method predict Gaussian point for each pixel, which causes the following disadvantages:
(1) The Gaussian points corresponding to the overlapping regions will cause artifact as shown in Fig (a). (2) Complex objects require more Gaussian points to represent, and the fixed number of predicted Gaussian points per region limits the quality of the image as shown in Fig 3 (b). So, we propose the Prune and Dilate Block (PD-Block) that adaptively predicts the number of Gaussian points at different areas. It can prune Gaussian points on overlapping parts and dilate the number Gaussian points on complex objects to improve rendering quality.
Prune and Dilate Block. We propose the Prune and Dilate Block (PD-Block), which can dilate the Gaussian point of complex instances and prune the redundant Gaussian as shown in Figure (c).
The core steps of PD-Block are: (1) Redundancy definition. Given range-view features, we evenly propose K centers in space, and the center feature computed by averaging its Z nearest points (36] We then calculate the pair-wise cosine similarity matrix S between the region feature and the center points. (2) Redundancy Pruning. We set threshold to generate mask M that considered 0 it is below this threshold and 1 if it is above this threshold. In addition, the point most similar to the center has always been retained. (3) Complexity Dilating, Based on mask, we can aggregate long-term features et and the local features cte, M * e + (1 - M) etc- Here, the long-term features et is extracted by a large kernal convolution, and the local features eto is the original range view features.

---

Unaligned Gaussian Adapter. The Gaussian adapter employs two convolutional blocks to convert features into segmentation E RC depth categories de E R', depth regression refinement d, R RGB color rgb E TR' alpha [Au, Ar], and optical flow [Ar, Ay, Az]. The activation functions for RGB color, alpha, scale, and rotation are consistent with those in [7]] The final depth per pixel is computed as dr Een I softmax(de) + dr-
IR! scale E R rotation E R UV-coordinate shifts
However, PD-Blocks effectively manage spatial computational redundancy by reallocating resources from simple scenes to more complex objects, allowing for Gaussian points that are not strictly pixel-aligned. For this reason, our Gaussian Adapter also predicts the offset of the uv coordinate [Au, Au]. The world coordinate [xt, y, =| RE dg * [u + Au, + Av, 1] V. The above operations are universal for any time and view, so we did not label the time and views for simplicity. Gaussian points of different viewing angles are all fused to render. In addition, we use the world coordinates at time and the predicted optical flow to get the world coordinates at time t+1, [21+1. Mt++1 2++1] [2x + Ars, V1 Ayt, + Azi. Rotational changes in object are interpreted as positional changes.
3.3 Static and Dynamic Decoupling
To learn geometry and motion information, DrivingRecon carefully designed the static and dynamic decoupling as shown in Fig. including both unsupervised and supervised manner.
Unsupervised manner. The views the driving scene are very sparse, meaning that only a limited number of cameras capture the same scene simultaneously. Hence, cross- voos-temporal view supervision essential. For dynamic objects, our algorithm predicts not only the current Gaussian of dynamic objects at time / but also predicts the motion flow of each Gaussian point. Therefore, will also use the next frame to supervise the predicted Gaussian points, i.e., dynamic reconstruction loss Lay- Additionally, we have the reconstruction constraint Cre. which involves rendering the image the same as the input. The dynamic and reconstruction constraint all use the LI constraint to constitute an unsupervised constraint Cre. We also employ cross-entropy loss Ce for the depth categories predicted by the Gaussian Adapter and LI loss L, for the refined depth y. In the field of autonomous
driving, depth supervision during training is considered unsupervised 65 20 [6].
Supervised manner. For autonomous driving, we also want to know the semantic properties of each Gaussian point in 4D space. So, cross-entropy loss is used to constrain the segmentation results predicted by Gaussian Adapter, i.e., Lang- Segmentation labels can be provided by some existing segmentation models, but we still consider them supervised for fair comparison. With the segmentation model, we can render the scene with camera parameters of adjacent timestamps and supervise only the static part, i.e., static reconstruction loss Cay- It is important to note that when supervising the rendering across the time sequence, we will not supervise the rendered image where the threshold value is less than a, as these pixels often do not overlap across the time sequence. In summary, the overall constraints for training DrivingRecon are:
Liotal = AoLe + Ap + Areefee + Car
Unsupervised
Aar + Aoeg
Supervised
where each À term balances the contribution of the respective loss component. Cer and Leeg used segmentation labels, which is not used for pre- training experiment. Other loss are considered unsuper- vised, which also allows DrivingRecon to achieve good performance. These collective regulations and constraints enable DrivingRecon effectively integrate geometry and motion information, enhancing its capacity for accurate scene reconstruction across time and perspectives.
Experiment
In this section, we evaluate the performance of DrivingRecon in terms of reconstruction and novel view synthesis, as well as explore potential applications. We also provide detailed information on the dataset setup, baseline methods, and implementation details.
5

---

Table 1: Comparison state-of-the-art methods the Waymo Open Dataset. PSNR, SSIM, and Depth RMSE (D-RMSE) are reported. Speed metrics are estimated on a single A100 GPU. ": reproduced by :: Non-sky region. not using segmentation supervision.
Dynamic-only SSIM
Full imaget
Infereace speed
Real-time rendering
Methods
PSNRT
D-RMSEL
PSNR
SSIMT
D-RMSEL
Time!
(>-200FSS)
Per-Scene Optimization methods
EmerNeRF
17.79
0.255
40.88
24.51
0.738
33.99
14min
3DGS 23
17.13
0.267
13.88
25.13
0.741
19.68
23min
PVG 5
15.51
0.128
15.91
22.38
0.661
13.01
27min
DefomableGS
68
17.10
0.266
12.14
25.29
0.761
14.79
29min
Generalizable feed-forword methods
LGM 44
19.58
0,443
9.43
23.59
0.691
8.02
0.06s
GS AS--LMIP 75
20.02
0.520
9.95
25.18
0.753
7.94
0.02s
SCahe" 42
20.51
0.532
8.82
25.72
0.783
5.62
0.42x
FrivingForward
48
21.97
0.622
7,42
26.32
0.774
5.79
0.2
STORM
65
22.10
0.624
7.50
26.38
0.794
5.48
0,18s
Ours
DriveRecon
23.11
0.641
7.32
27.23
0:819
5.23
0.08s
DriveRecon
24.01
0.662
7.03
27.52
0.833
5.14
0.08s
4.1 Datasets.
Following
[6], use both the Waymo Open dataset 45 and the nuScenes [1 to test the algorithm's ' performance. The rendered image resolution of DrivingRecon is 160 x 240 as same For nuScenes dataset, the rendered image resolution of DrivingRecon is 224 400 same All training and testing validation are consistent with the official ones.
48
42
65
665]
as 56
4.2 Training Details.
The model is trained 24 NVIDIA A100 (80G) GPUs for 50000 iterations, all about 24 gpu days. batch size 2 for each GPU is used under bíloat16 precision, resulting in an effective batch size of 48. The AdamW optimizer is employed with learning rate of 4 10-4 and weight decay of 0.05. Are, Åe, Xr, Adr. Asr, Aseg are set as 1.0, 0.1, 0.1, 0.1, 0.1, 0.1, respectively. Following [65), The input to model consists of 4 context timesteps, evenly spaced at t + Os, t 0.5s, t + 1.0s, and + 1.5s, where t is a randomly chosen starting timestep. These balance parameters are based on our experience.
4.3 Main Results
We compare our method against both two categories of approaches: per-scene optimization methods and feed-forward models. For per-scene optimization, we evaluate against both NeRF-based and 3DGS-basee approach, including EmerNeRF (66), 3DGS [23), PVG [5), and DeformableGS (68).
Since LiDAR data is not provided at test time in our setup, these baselines is also without LiDAR supervision to ensure a fair comparison. In the second category, we compare against recent large reconstruction models, including LGM (47), GS-LRM 77), SCube (42) DrivingForward (48).
STORM [65]. SCube and DrivingForward were modified to the same resolution and test protocol using their officially provided code.
We present the quantitative results in Tab. Compared to per-scene optimization methods, the large reconstruction models achieves comparable performance in both dynamic regions and full images in terms of photorcalism. geometry, and inference speed. Notably, compared to other generalizable feed-forward models, DriveRecon demonstrates robust ability to model scene dynamics and process multi multi-timestep, multi-view images. DriveRecon also achieves the best performance when it has no segmentation supervision, that is, pure unsupervised. In fact, both Scube and STORM use different forms of segmentation supervision. Besides, DriveRecon achieves these improvements while reducing inference time to just 0.08 second. The sota method STORM has 100.60M parameters, and DriveRecon only has 53.37M parameters. This is because we used the shared image encoder and the range view decoder. It ensures that our model parameters and reasoning are significantly efficient.
To better clarify the effectiveness our algorithm. We compare Scube and OmniScene, two new generalizable Gaussian methods. STORM is not open source is not compared. As shown in Fig, 4

---

OmniScene
Scube
Ours
Figure 4: Reconstruction quantitative comparison. The red box indicates the presence of artifacts in the overlap area of multi-view images, due to Gaussian prediction at per-pixel level (zoom in for the best view.)
Scube and OmniScene have serious artifacts in overlapping areas. This is because these method predict Gaussian point for each pixel, and the same location the overlapping region may correspond to multiple Gaussian points. Our PD-Block can dynamically remove these redundant points, and the range-view decoder can better integrate different views. This responds to the motivation of our paper.
Table 2: Comparison to state-of-the-art methods on the nuScenes. *: reproduced by us. Pearson Correlation Coefficient (PCC) quantifies the statistical relationship between the predicted depth and ground-truth depth as the scale-invariant metrie [56].
Method
PSNR
SSIM
LPIPS
PCC1
pixelSplat
21.51
0.616
0.372
0.001
MVSplat 6
21.61
0.658
0.295
0.181
DrivingFFowardd
48
24.32
0.732
0.229
0.766
Omni-Scene
56
24.27
0.736
0.237
0.804
DriveRecon
25.42
0.782
0.201
0.825
simultaneously nuScenes. The DrivingForward reproduced using the same protocol as [56). STORM is not open source, and Scube relies heavily on more fine fine-grained Occ. So, can't 't reproduce these method nuScenes. As shown in Tab. 2 we have also demonstrated the advance of our approach. This proves that our PD-Block and dynamic and static decoupling are very efficient.
Further, we compare recent DrivingForward 488 and Omni-Scene [56)
4.4 Ablation study
To assess the effectiveness of our proposed algorithm, we conducted a series of ablation experiments. The key components under evaluation include the PD-Block and Dynamic and Static Decoupling(DS).
Table
Ablation of DrivingRecon.
As shown in Tab. each module contributes significant performance improvements. Notably, the PD-Block achieves the highest enhancement. This improvement stems from two primary fac- tors: (1) optimized distribution of compu- tational resources based on spatial complexity. where more Gaussian points allocated to complex regions while simpler backgrounds receive fewer points; (2) enhanced multi-view integra- tion within broad field of view. The DS-R mechanism also led marked improvements, largely attributed to the use of cross-temporal supervision for better dynamic and static object differentia- tion. Qualitative visualization of the new view can be seen in Fig. proved that we can render geometrically consistent new perspectives in different time.
PSNR
SSIM
LPIPS
all w/o PD-Block
27.52
0.83
0.16
26.21
0.78
0.24
w/o DS
26.44
0.79
0.20
4.5 Potential application
Vehicle adaptation. The introduction of a new car model may result in changes in camera parameters, such as camera type (intrinsic parameters) and camera placement (extrinsic parameters) [53] 35).
7

---

Different rendering views
Different time stamps
Figure 5: Novel view rendering. Based on the predicted Gaussians, we render different views at different times. The novel views are of very high quality and very high spatio-temporal consistency (zoom in for the best view.)
(a)
(b)
Figure 6: Scene editing. We can insert the object in the scene, and ensure time consistency. Table 4: Comparison of different approaches on domain generalization protocols, where stands for using aligned intrinsic parameters, stands for randomly augmenting camera extrinsic parameters.
Waymo nuScenes
Turget Domain (nuScenes)
Method
mAPt
mATE,
mASE
mAOEL
NDS t
Oracle
0.475
0.577
0.177
0.147
0.587
DG-BEV
0.303
0.689
0.218
0.171
0.472
PD-BEV
0.311
0.686
0.216
0.170
0.478
Ours
0.305
0,690
0.219
0.167
0.471
Ours
0.323
0.675
0.212
0.166
0.490
The 4D reconstruction model is capable rendering images with different camera parameters to mitigate the potential overfitting of these parameters. To achieve this, rendered images Waymo with random intrinsic parameters and performed random rendering novel views as a form of data augmentation. Based PD-BEV used this jointly rendered and original data to train the BEVDepth on Waymo, following the approach of [53 35 is important to note that our rendered images also undergo augmentation pipeline part of the detection algorithm, including resizing and cropping.

---

Table 5: Performance gain of our method for joint perception, prediction, and planning.
Method
Detection
Tracking AMOTA1
Future Occupancy Prediction
NDS f mAP1
AMOTP!
IDSL
ToU-n."
loU-f.
VPQ-n.t
VPQ-f1
UniAD
49.36
37.96
38.3
1.32
1054
62.8
40.1
54.6
33.9
ViDAR
52.57
42.33
42.0
1.25
991
65.4
42.1
57.3
36.4
Ours+
53.21
43.21
42.9
1.18
948
66.5
43.3
58.2
37.3
Method
Mapping loU-roadt
Motion Forecasting minFDE.
Planning
loU-lanet
minADE.
MR
avg.L2
avg. Col.C
UniAD
31.3
69.1
0.75
1.08
0.158
1.12
0.27
ViDAR
33.2
71.4
0.67
0.99
0.149
0.91
0.23
Ours+
33,9
72.1
0.60
0.89
0.138
0.84
0.19
As demonstrated in Tab. when employ both camera intrinsic and extrinsic parameter augmenta- tion, we observe significant improvement performance. However, the use of only camera intrinsic parameter augmentation did not yield good results, due to the superior ability of virtual depth in addressing the issue of camera intrinsic parameters. The utilization of multiple extrinsic parameters helps the algorithm learn the stereo relationship between cameras more effectively.
Pre-training model. The 4D reconstruction network capable of understanding the geometric information of the scene, the motion trajectory of dynamic objects, and the semantic information. These capabilities are reflected the encoding of images, where the weights these encoders are shared. We replaced our encoder with the ResNet-50,, which is commonly used base network for many algorithms as same as [67]. Following VIDAR P we then retrained the 4D reconstruction network using this configuration, without using any segmentation annotations, resulting in completely unsupervised pre- pre-training. Subsequently, we replaced the encoder of UniAD [99 with our pre- pre-trained model and fine-tuned it the nuScenes dataset. The results, presented in Tab. 5 demonstrate that
our pre-trained model achieved better performance compared to ViDAR [67.. highlighting the ability of our algorithm to leverage large-scale unsupervised data for pre-training and improving multiple downstream tasks.
The 4D scene reconstruction model enables us to obtain comprehensive 4D geometry information of a scene, which allows for the removal, insertion, and control of objects within the scene. As shown in Fig. 6 we added billboards with people's 's faces to fixed positions in the scene, representing a corner case where cars come to a stop. As can be seen from the figure, the scenario we created exhibits high level of temporal consistency.
Scene editing.
Conclusion
The paper introduces DrivingRecon, a novel 4D Gaussian Reconstruction Model for fast 4D recon- struction of driving scenes using surround-view video inputs. The entire framework uses shared image encoder and range view decoder. A key innovation is the Prune and Dilate Block (PD-Block), which prunes redundant Gaussian points from adjacent views and dilates points around complex edges, enhancing the reconstruction of dynamic and static objects. Additionally, dynamic-static rendering approach using optical flow prediction allows for better supervision of moving objects across time sequences. DrivingRecon shows superior performance in scene reconstruction and novel view synthesis compared to existing methods. It is particularly effective for tasks such model pre-training, vehicle adaptation, and scene editing. We quantitatively evaluated pre-training and vehicle adaptation and made significant improvements.
Acknowledgments
This work is supported by National Natural Science Foundation China (No. 62206068), HKUST- HKUST(GZZ Cross-Campus Collaborative Research Scheme (Project No. C036) and Guangdong Provincial Department of Science and Technology's '1+1+1' Joint Funding Program for Guangdong- Hong Kong Universities.

---

References
[1] H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijhom. nuscenes: multimodal dataset. In Proc. IEEE CVPR, 2020.
[2] D. Charatan, S. Li, A. Tagliasacchi, and V. Sitzmann. pixelsplat. In arXiv preprint WIXi:22322.22337. 2023.
[3] D. Charatan, S. I.. Li, A. Tagliasacchi, and V. Sitzmann. pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction. In Proc. CVPR, pages 19457-19467, 2024.
[4] A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su. Tensorf: Tensorial radiance fields. In Proc. ECCV, pages 333-350, 2022.
[5] Y. Chen, C. Gu, J. Jiang, X. Zhu, and L. Zhang, Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering. In ArXiv, 2023.
[6] Y. Chen, H. Xu, C. Zheng, B. Zhuang, M. Pollefeys, A. Geiger, T.-J. Cham, and J. Cai. Mvsplat. In arXiv preprini urXiv:2403. 14627, 2024.
[7] Z. Chen, J. Yang, J. Huang, R. de Lutio, M. Esturo, B. Ivanovic, O. Litany, Z. Gojcic, Fidler, M. Pavone, et al. Omnire. arXiv preprint arXiv:2408. 16760, 2024.
[8] J. Cheng, Y. Chen, Q. Zhang, L. Gan, C. Liu, and M. Liu. Real-time trajectory planning for autonomous driving with gaussian process and incremental refinement. In Proc. ICRA, pages 8999-9000 2022.
[9] J. Cheng, X. Mei, and M. Liu. Forecast-MAB: Self-supervised pre-training for motion forecast- ing with masked autoencoders. In Proc. ICCV, 2023.
[10] D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta. Parting with misconceptions about learning-based vehicle motion planning. In Proc. CoRL, 2023.
[11] M. Deitke, D. Schwenk, J. Salvador, L.. Weihs, O. Michel, F. VanderBilt, I. Schmidt, K. Ehsani,
A. Kembhavi, and A. Farhadi. Objaverse. In Proc. IEEE/CVF CVPR, pages 13142-13153, 2023.
[12] A. Dosovitskiy, G. Ros, F. Codevilla, A. M. López, and V. Koltun. Carla: An open urban driving simulator. In Proc. CoRL, 2017.
[13] L. Fan, X. Xiong, F. Wang, N. Wang, and Z. Zhang. Rangedet: In defense of range view for lidar-based 3d object detection. In Proc. IEEE ICCV, pages 2918-2927. 2021.
[14] Gu, C. Hu, T. Zhang, X. Chen, Y. Wang, Y. Wang, and H. Zhao. Vip3d. In arXiv preprint arXiv:2208.0/s: 2022.
[15] Guo, N. Deng, X. Li, Y. Bai, B. Shi, C. Wang, C. Ding, D. Wang, and Y. Li. Streetsurf. In ArXiv, 2023.
[16] Y. Hong, K. Zhang, Gu, S. Bi, Y. Zhou, D. Liu, F. Liu, K. Sunkavalli, T. Bui, and H. Tan. Lrm: Large reconstruction model for single image to 3d. In arXiv preprint arXiv:231 1.0:2000000440 2023.
[17] A. Hu, Z. Murez, N. Mohan, S. Dudas, J. Hawke, V. Badrinarayanan, R. Cipolla, and A. Kendall. Fiery. In Proc. IEEE ICCV, 2021.
[18] S. Hu, L. Chen, P. Wu, H. J. Yan, and D. Tao. St-p3. In Proc. ECCV, 2022.
[19] Y. Hu, J. Yang, I.. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, al. Planning-oriented autonomous driving. In Proc. IEEE CVPR, pages 17853-17862, 2023.
[20] N. Huang, X. Wei, W. Zheng, P. An, M. Lu, W. Zhan, M. Tomizuka, K. Keutzer, and S. Zhang. 5 Gaussian: Self-Suuerrised Street Gaussians for Autonomous Driving. In arXiv preprint arXiv:2405.20323. 2024.
10

---

[21] Y. Huang. W. Zheng. Y. Zhang, J. Zhou, and J. Lu. Tri Tripperspective view. In Proc. IEEE CVPR, pages 9223-9232, 2023.
[22] B. Jiang, S. Chen, Q. Xu, B. Liao, J. Chen, H. Zhou, Q Zhang, W. Liu, C. Huang, and X. Wang. Vad. In arXiv preprint (XXi:2333./2077 2023.
[23] B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis. 3d gaussian splatting for real-time radiance field rendering. In ACM Trans. Graph., volume 42, pages 1-14, 2023.
[24] L. Kong, Y. Liu, R. Chen, Ma, X. Zhu, Li, Y. Hou, Y. Qiao, and Z. Liu. Rethinking range view representation for lidar segmentation. In Proc. IEEE ICCV, pages 228-240, 2023.
[25] Z. Li, W. Wang. Li, E. Xie, C. Sima, T. Lu, Q. Yu, and J. Dai. Bevformer. Proc. ECCV, 2022.
[26] M. Liang, B. Yang, W. Zeng, Chen, R. Hu, S. Casas, and Urtasun. Pnpnet. In Proc. IEEE CVPR, 2020.
[27] J. Liu, J. Han, L. Liu, A. I. Aviles-Rivero, C. Jiang. Z. Liu, and H. Wang. Mamba4d: Efficient 4d point cloud video understanding with disentangled spatial-tempon state space models. In CVPR, pages 17626-17636, 2025.
[28] J. Liu, Z. Huang, M. Liu, T. Deng, F. Nex, H. Cheng, and H. Wang. Topolidm: Topology-awe aware lidar diffusion models for interpretable and realistic lidar point cloud generation. arXiv preprint arXiv:2507.22454 2025.
[29] J. Liu, G. Wang, W. Ye, C. Jiang, J. Han, Z. Liu, G. Zhang, D. Du, and H. Wang. Difflow3d: Toward robust uncertainty-aware scene flow estimation with iterative diffusion- refinement. In CVPR, pages 15109-15119, 2024.
[30] Liu, W. Ye, Wang, C. Jiang, L. Pan, Han, Z. Liu, G. Zhang, and H. Wang. Difflow3d: Hierarchical diffusion models for uncertainty-awer 3d scene flow estimation. IEEE TPMAI, 2025.
[3]] J. Liu, D. Zhuo, Z. Feng, S. Zhu, C. Peng, Z. Liu, and H. Wang. Dvlo: Deep visual-lidar odometry with seal-to-global feature fusion and bi-directional structure aligmment. ECCV, pages 475-493. Springer, 2025.
[32] Lu, Y. Xu, G.-S. Chen, H. Li, K.-Y. Lin, and C. Jiang, Urban radiance field representation with deformable neural mesh primitives. In Proc. IEEE ICCV, pages 465-476, 2023.
[33] H. Lu, Z. Liu, G. Jiang, Y. Luo, S. Chen, Y. Zhang, and Y.C. Chen. Uniugp: Unifying understanding, generation, and planing for end-to-end autonomous driving. arXiv preprins arXiv:2512.09866 2025.
[34] H. Lu, Z. Ma. G. Jiang, W. Ge, B. Li, Y. Cai, W. Zheng, Y. Zhang, and Y. Chen. 4d driving scene generation with stereo forcing. arXiv preprint arXiv:2509.20251, 2025.
[35] H. Lu, Y. Zhang, Q. Lian, D. Du, and Y. Chen. Towards generalizable multi-camera 3d object detection via perspective debiasing. In AAAI, 2025.
[36] X. Ma. Y. Zhou, H. Wang, C. Qin, B. Sun, Liu, and Y. Fu. Image points. In ICLR. 2023.
[37] B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng. Nerf. In Communications of the ACM, pages 99-106, 2021.
[38] T. Müller, A. Evans, C. Schied, and A. Keller. Instant neural graphics primitives with multiresolution hash encoding. In ACM transactions on graphics (TOG), pages 1-15, 2022.
[39] X. Pan, J. Yao, H. Kou, T. Wu, and C. Xiao. Harmonicnerf: Geometry-informed synthetic view augmentation for 3d scene reconstruction in driving scenarios. In ACM MM, pages 5987-5996. 2024.
11

---

[40] K. Rematas, A. Liu. P. P. Srinivasan, J. T. Barron. A. Tagliasacchi, T. A. Funkhouser, and Ferrari. Urban radiance fields. In Proc. IEEE CVPR, pages 12922-12932, 2021.
[41] J. Ren, K. Xie, A. Mirzaei, H. Liang X. Zeng, K. Kreis, Z. Liu, A. Torralba, S. Fidler, S. W. Kim, et al. L4gm: Large 4d gaussian reconstruction model. In arXiv preprint arXiv:2406.10324 2024.
[42] X. Ren, Lu, J. Z. Wu, H. Ling, M. Chen, S. Fidler, F. Williams, J. Huang, et al. Scube: Instant large-scale scene reconstruction using voxsplats. In NeurIPS, 2024.
[43] S. Shah, Dey, C. Lovett, and Kapoor. Airsim. In International Symposium on Field and Service Robotics, 2017.
[44] Q. Shen, X. Yi, Z. Wu, P. Zhou, H. Zhang, S. Yan, and X. Wang. Gamba: Marry gaussian splatting with mamba for single view 3d reconstruction. In arXiv preprint arXiv:2403. 18795, 2024.
[45] Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, Joshi, S. Zhao, Cheng, Y. Zhang J. Shlens, Z. Chen, and Anguelov. Scalability in perception for autonomous driving. In Waymo Open Dataset, 2020.
[46] S. Szymanowicz. E. Insafutdinov, C. Zheng, D. Campbell, J. F. Henriques, C. Rupprecht, and A. Vedaldi. Flash3d: Feed-forward generalisable 3d scene reconstruction from a single image. In arXiv preprint arXiv:2406.0m 04343, 2024.
[47] Tang, Z. Chen, X. Chen. T. Wang, G. Zeng, and Z. Liu. Lgm: Large multi- gaussian model for high- high-resolution 3d content creation. In Proc. ECCV, pages 1-18. Springer, 2024.
[48] Q. Tian, X. Tan, Y. Xie, and L. Ma. Drivingforward: Feed-forward 3d gaussian splatting for driving scene reconstruction from flexible surround-view input. AAAI, 2025.
[49] H. Turki, D. Ramanan, and M. Satyanarayanan. Mega-nerf: Scalable construction of large-scale nerfs. In Proc. IEEE/CVF CVPR, pages 12912-12921, 2021.
[50] H. Turki, J. Y. Zhang, Ferroni, and D. Ramanan. Suds: Scalable urban dynamic scenes. In Proc. IEEE/CVF CVPR, pages 12375-12385, 2023.
[51] P. Wang, X. Chen, T. Chen, S. Venugopalan, Z. Wang, et al. Is attention all that nerf needs? In arXiv preprint arXiv:2207. 13298, 2022.
[52] Q Wang, Z. Wang, K. Genova, P. Srinivasan, H. Zhou, J. Barron, R. Martin-Brualla, N. Snavely, and T. Funkhouser. Ibrnet. In Proc. IEEE CVPR, pages 4690-4699, 2021.
[53] Wang, X. Zhao, H.-M. Xu, Z. Chen, D. Yu, J. Chang. Z. Yang, and F. Zhao. Towards domain generalization for multi-view 3d object detection in bird-eye-view. In Proc. CVPR, pages 13333-13342, 2023.
[54] Z. Wang, B. Li, C. Wang, and S. Scherer. Airshot: Efficient few-shot detection for autonomous exploration. In 2024 JEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1654-11661. IEEE, 2024.
[55] Z. Wang, J. Tan, T. Khurana, N. Peri, and D. Ramanan. Monofusion: Sparse-view 4d recon- struction via monocular fusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8252-8263, 2025.
[56] D. Wei, Z. Li, and P. Liu. Omni-scene: comni-gaussioe representation for ego-centric sparse-view scene reconstruction. In Proc. IEEE CVPR, 2025.
[57] X. Wei, R. Zhang, J. Wu, J. Liu, M. Lu, Y. Guo, and S. Zhang. Noc: High-quality neural object cloning. In arXiv preprint arXiv:2309. 12790, 2023.
[58] Y. Wei, L. Zhao, W. Zheng, 7. Zhu, J. Zhou, and Lu. Surroundoce: Multi-camera 3d occupancy prediction. In ICCV, pages 21729-21740, 2023.
12

---

[59] C.-Y. Wu, J. Johnson, J. Malik, C. Feichtenhofer. and G. Gkioxari. Multiview compressive coding for 3d reconstruction. In Proc. IEEE CVPR. pages 9065-9075, 2023.
[60] Z. Wu, T. Liu, L. Luo, Z. Zhong, J. Chen, H. Xiao, C. Hou, H. Lou, Y. Chen, R. Yang. Huang,
X. Ye, Z. Yan, Y. Shi, Y. Liao, and H. Zhao. Mars: An instance-aware, modular and realistic simulator. In CICAI, 2023.
[61] M. Xie, S. Zeng. X. Chang, X. Liu, Z. Pan, M. Xu, and X. Wei. Seggrowgraph: Learning lane October 2025.
topology as chain of graph expansions. In ICCV, pages
[62] Z. Xie, J. Zhang, W. Li, Zhang, and L. Zhang. S-nerf: Neural radiance fields for street views. In arXiv preprint arXiv:2303 007499, 2023.
[63] Y. Xu, Z. Shi, W. Yifan, H. Chen, C. Yang, S. Peng. Y. Shen, and G. Wetzstein, Grm: Large gaussian reconstruction model. In arXiv preprint arXiv:2403. 14621, 2024.
[64] Y. Yan, Lin, C. Zhou, W. Wang, H. Sun, K. Zhan, X. Lang, X. Zhou, and S. Peng. Street gaussians for modeling dynamic urban scenes. In ArXiv, volume abs/2401.01339, 2024.
[65] J. Yang, J. Huang, Y. Chen, Y. Wang. B. Li, Y. You, A. Sharma, M. Igl, P. Karkus, D. Xu, et al. Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes. Proc. ICLR, 2024.
[66] J. Yang, B. Ivanovic, O. Litany, X. Weng, S. W. Kim, B. Li, T. Che, D. Xu, S. Fidler, M. Pavone, and Y. Wang. Emernerf: Emergent spatial-temporal scene decomposition via self-supervision. 2023.
[67] 7. Yang, L. Chen, Y. Sun, and H. Li. Visual point cloud forecasting, In Proc. IEEE CVPR, 2024.
[68] Z. Yang. X. Gao, W. Zhou, Jiao, Y. Zhang, and X. Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. In ArXiv, volume abs/2309. 2023.
[69] J. Yao, J. Zhang, X. Pan, T. Wu, and C. Xiao. Depthssc: Monocular 3d semantic scene completion via depth-spatial alignment and voxel adaptation. In WACV, pages 2154-2163. IEEE, 2025.
[70] A. Yu, V. Ye, M. Tancik, and A. Kanazawa. pixelnerf: Neural radiance fields. In Proc. IEEE/CVF CVPR, pages
2021.
[71] S. Zeng, X. Chang, X. Liu, Z. Pan, and X. Wei. Driving with prior maps: Unified vector prior encoding for autonomous vehicle mapping. arXiv preprint arXiv:2409.05355 09900055555, 2024.
[72] S. Zeng. X. Chang, M. Xie, X. Liu, Y. Bai, Z. Pan, M. Xu, and X. Wei. Futuresightdrive: Think- ing visually with spatio-tempord cot for autonomous driving. arXiv preprint arXh::5505.17655, 2025.
[73] S. Zeng, D. Qi, X. Chang, F. Xiong, S. Xie, X. Wu, S. Liang, M. Xu, and X. Wei. Janusvin: Decoupling semanties and spatiality with dual implicit memory for vision-language navigation. arXiv preprint 2rXiv:2509.225444 2025.
[74] J.-T. Zhai, Z. Feng. J. Du, Y. Mao. J.-J. Liu, Z. Tan, Y. Zhang, X. Ye, and J. Wang. Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscenes. In arXiv preprint arXiv:2305. 10430, 2023.
[75] K. Zhang, S. Bi, H. Tan, Y. Xiangli, N. Zhao, K. Sunkavalli, and Z. Xu. Gs-Irm: Large reconstruction model for 3d gaussian splatting, In arXiv preprint arXiv:2404. 19702, 2024.
[76] Y. Zhang, Z. Zhu, W. Zheng. J. Huang, G. Huang, J. Zhou, and J. Lu. Beverse: Unified perception and prediction in birds-eye-view for vision-centric autonomous driving. In arXiv preprint arXiv:2205.001 09740.9944, 2022.
[77] X. Zhou, Z. Lin, X. Shan, Y. Wang, D. Sun, and M.-H. Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes. In ArXiv, volume abs/2312.07920, 2023.
13

---

NeurIPS Paper Checklist
The checklist is designed encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.
Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:
You should answer [Yes], [No], or [NA].
[NA] means either that the question Not Applicable for that particular paper or the relevant information is Not Available.
Please provide short (1-2 sentence) justification right after your answer (even for NA).
The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include (after eventual revisions) with the final version of your paper, and its final version will published with the paper.
The reviewers of your paper will be asked use the checklist as one of the factors their evaluation. While "[Yes] is generally preferable to "[No]" ", it is perfectly acceptable to answer "[No] providedd proper justification is given (e.g.. "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering binary way, we
"[No] or "[NA]" is not grounds for rejection. While the questions are phrased acknowledge that the true answer is often more nuanced, please just use your best judgment and write justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.
IMPORTANT, please:
Delete this instruction block, but keep the section heading "NeurIPS Paper Checklist",
Keep the checklist subsection headings, questions/answers and guidelines below.
Do not modify the questions and only use the provided macros for your answers.
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: On Paper
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results be expected to generalize other settings.
is fine include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
Show on Appendix.
14

---

Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, c.g., if the approach was only tested few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: Application-driven
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
Our code will be made publicly available soon.
Guidelines:
The answer NA means that the paper does not include experiments.
15

---

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily new algorithm, the paper should make it clear how to reproduce that algorithm. (b) the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: Dataset is open.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (nttps://nips.co
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
7 /ttppp.cc/pbllc/guudeeee http://odebbibiiiooooooooooooooo
for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
16

---

Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Question:
Answer: [Yes]
Justification: Show main paper.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification: Show on main paper.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results accompanied by error bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
OK to report 1-sigma error bars, but one should state The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that out of range (e.g. negative error rates).
If error bars reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: Show on main paper.
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
17

---

The paper should provide the amount of compute required for each of the individual experimental runs as well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics
Answer: [Yes]
Justification: The authors have reviewed the NeurIPS Code of Ethics and confirm that the research described in this paper complies with the Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make to preserve anonymity (e.g., if there is special consid- eration due to laws or regulations their jurisdiction).
Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [NA].
Justification: NA
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has socictal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [Yes]
Justification: On appendix.
Guidelines:
18

---

The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [NA]
Justification: NA
Guidelines:
The answer NA means that the paper does existing
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset used and, possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g.. website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that re- re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. If this information is not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA].
Justification: Sure
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include anonymized zip file.
14. Crowdsourcing and research with human subjects
For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and sereenshots, if applicable, as well details about compensation (if any)?
Question:
Answer: [NA].
19

---

Justification: NA
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According to the NeurIPS Code of Ethics, workers involved data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]-
Justification: NA
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, or originality the research, declaration is not required.
Answer: [NA].
Justification: NA
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to L.LM policy http:s/s/eeerpppeccc///oferereep for what should or should not be described.
20


Reviews:
[{"review_id": "iJ1b131fOD", "reviewer": "Reviewer_KAzz", "summary": "This paper presents DrivingRecon, a feed-forward model for 4D scene reconstruction in autonomous driving. By using a shared 2D encoder and range-view decoder, the model efficiently fuses multi-view images to generate 4D Gaussian representations. The core innovations are the PD-Block, which optimizes Gaussian point distribution by pruning overlaps and dilating complex regions, and dynamic-static decoupling, which enables cross-temporal supervision for better motion modeling. Extensive evaluations on Waymo and nuScenes datasets demonstrate superior performance in reconstruction quality and inference speed, outperforming both per-scene optimization and generalizable feed-forward methods.", "questions": "1.\tDynamic Object Resolution: How does DrivingRecon handle small, fast-moving objects (e.g., bicycles) without explicit motion prediction modules? Are there plans to integrate motion-aware Gaussian updates?\n2.\tComputational Scalability: For large urban scenes with thousands of dynamic objects, does the PD-Block maintain efficiency? Could spatial partitioning (e.g., octrees) further optimize inference?\n3.\tTemporal Consistency in Long Sequences：For extended driving sequences (e.g., 10+ minutes), how does the model maintain temporal consistency without cumulative errors in Gaussian point trajectories?", "limitations": "Does this paper have an Appendix?", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\n1.\tOriginality of Approach: The PD-Block addresses redundancy in multi-view Gaussian fusion and dynamically allocates computational resources, a novel solution to overlapping artifacts and fixed-point limitations in prior work.\n2.\tComprehensive Evaluation: The paper compares DrivingRecon against a wide range of baselines (per-scene optimization and feed-forward models) and provides ablation studies for PD-Block and dynamic-static decoupling, validating component effectiveness.\n3.\tEfficiency: DrivingRecon achieves faster inference (0.08s) with fewer parameters (53.37M) than sota methods like STORM (100.60M), enabled by shared encoders and range-view decoders.\nWeaknesses\n\n1.\tInadequate Validation for Fast-Moving or Small Dynamic Objects：while dynamic-static decoupling is proposed, the model’s reconstruction quality for fast-moving objects (e.g., motorcycles, pedestrians) or small entities (e.g., bicycles) is not explicitly tested. The optical flow supervision (Ldy) may struggle with high-velocity motions or occlusions.\n2.\tAdaptive Limitations of PD-Block：The PD-Block relies on manually set thresholds (e.g., τ for redundancy pruning) and empirical parameters (e.g., K centers and Z nearest points), lacking dynamic adaptation to varying scene complexities. For instance, fixed thresholds may incorrectly prune critical Gaussian points in dense traffic or retain redundant points in complex object edges, degrading reconstruction fidelity. The nearest-point aggregation strategy struggles to generalize across scene densities (e.g., urban centers vs. suburban roads), potentially causing feature under-fitting in sparse areas or computational redundancy in dense regions.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable time and comments. I think my following reply will alleviate your concerns:\n\n **W1: Validation for Fast-Moving or Small Dynamic Objects.**  I have supplemented more ablation experiments for different speeds and small dynamic objects (people and two-wheelers in driving scenarios are considered small dynamic objects). The experimental setting is the same as that in Table 1 of original paper. We use box labels （with the speed and different objects） prompt the sam to generate the segmentation.\n\n| Category | DriveRecon | DriveRecon w/o DS | DriveRecon w/o PD-Block |\n| --- | --- | --- | --- |\n|  | **PSNR↑ / SSIM↑ / LPIPS↓** | **PSNR↑ / SSIM↑ / LPIPS↓** | **PSNR↑ / SSIM↑ / LPIPS↓** |\n| **Speed > 10 m/s** | 24.75 / 0.78 / 0.24 | 23.35 / 0.70 / 0.30 | 22.85 / 0.68 / 0.32 |\n| **Speed < 10 m/s** | 26.53 / 0.84 / 0.16 | 25.63 / 0.82 / 0.19 | 25.13 / 0.81 / 0.21 |\n| **Person** | 26.51 / 0.79 / 0.20 | 25.62 / 0.77 / 0.23 | 25.15 / 0.76 / 0.25 |\n| **Two-wheeler** | 26.14 / 0.78 / 0.21 | 25.21 / 0.76 / 0.24 | 24.77 / 0.75 / 0.26 |\n| **Others** | 27.24 / 0.82 / 0.17 | 26.33 / 0.80 / 0.20 | 25.86 / 0.79 / 0.22 |\n \n**W2: Adaptive Limitations of PD-Block.**  We conducted ablation experiments on the three most important parameters and found that our algorithm is robust to the parameters. Although the receptive field and threshold may bring about some changes, there is still a significant improvement compared to the Baseline, which indicates that our cropping and dilation mechanisms are highly effective.\n| Parameters | PSNR  | SSIM  | RMSE |\n|------------|-------|-------|------|\n| base       | 23.70 | 0.590 | 8.92 |\n| τ (0.1)    | 24.10 | 0.620 | 8.50 |\n| τ (0.3)    | 25.22 | 0.643 | 7.62 |\n| τ (0.5)    | 25.50 | 0.640 | 7.80 |\n| τ (0.7)    | 25.44 | 0.642 | 7.77 |\n| τ (0.9)    | 24.85 | 0.610 | 8.35 |\n| K=3, Z=9   | 25.00 | 0.662 | 7.03 | \n| K=5, Z=25  | 25.80 | 0.645 | 7.50 |\n| K=7, Z=49  | 25.40 | 0.630 | 7.95 |\n\n**Q1: Dynamic Object Resolution.** Our model will additionally predict the optical flow parameters to simulate the movement of objects. Therefore, our algorithm performs better in modeling dynamic objects compared to existing methods, as shown in Table 1. When Motion-aware Gaussian is better at further enhancing small objects, it will be further considered by us. Thank you for your suggestion.\n\n**Q2: Computational Scalability.** We ablated the inference speeds of different resolutions. We found that the increased time of our PD-Block was very small and could be ignored.\n\n| Resolution  | Time (s) | Time without PD-Block (s) |\n|-------------|----------|----------------------------|\n| 160 x 240   | 0.08     | 0.07                       |\n| 320 x 480   | 0.17     | 0.16                       |\n\n\n**Q3: Temporal Consistency in Long Sequences.** We have the geometric evaluation in Table 2, which indicates that our geometric consistency is also the best relative to the existing work.\n\n**Q3: Does this paper have an Appendix?** We have appendix, please check."}, {"review_id": "rXblyBwdg7", "reviewer": "Reviewer_2Js5", "summary": "This paper introduces a feedforward model that infers 3D Gaussians of driving scenes from multi-view images. It employs PD-Block and dynamic-static decoupling for a reasonable representation of the scene. The paper also explores downstream tasks such as rendering with changed camera poses and intrinsics, pre-training for end-to-end driving, and scene editing. Furthermore, the model achieves state-of-the-art performance when compared to other feedforward reconstruction models for autonomous driving scenarios.", "questions": "What is depth categories in line 135?\n\nWhat do L_dr and L_sr in line 171 stand for?\n\nPlease refer to other questions in weaknesses.", "limitations": "The reconstruction quality of per-scene optimization and rendering quality of images can be improved. And the differences between existing SoTA methods should be clarified.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1.  The PD-Block introduces a novel mechanism for managing Gaussian points by learning to both prune and dilate them, thereby enabling a more flexible, non-pixel-aligned feedforward gaussian representation.\n2.  The method outperforms existing feedforward driving reconstruction approaches. This article also uses its image encoder to explore pre-training for end-to-end autonomous driving.\n3.  A static-dynamic separation strategy for unsupervised learning of autonomous driving scene reconstruction.\n4.  Extensive experiments, including reconstruction speed, rendering image quality, and rendering depth comparison.\n\nWeaknesses\n1. The images in Figure 5 and Figure 6 are of low rendering quality. Additionally, the presentation of novel views feels insufficiently clear. For reference, works like FreeVS, StreetCrafter, and ReconDreamer[1,2,3] utilize specific ego-vehicle lane change distances (in meters) for visualization.\n2. Regarding scene editing tasks, is it possible to elevate the scope to include modifications of vehicles within the scene like Street Gaussians[4]?  The current article solely showcases the capability of adding novel objects (billboards).\n3. In Table 1, why is the reconstruction quality of per-scene optimization methods so low? Is it because the training iterations were limited? For PVG, if it didn't use LiDAR information, did it utilize SfM to extract the initial point cloud for the scene?\t\n\nReference\n\n[1]Wang, Qitai, et al. \"Freevs: Generative view synthesis on free driving trajectory.\" arXiv preprint arXiv:2410.18079 (2024).\n\n[2]Ni, Chaojun, et al. \"Recondreamer: Crafting world models for driving scene reconstruction via online restoration.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[3]Yan, Yunzhi, et al. \"Streetcrafter: Street view synthesis with controllable video diffusion models.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[4]Yan, Yunzhi, et al. \"Street gaussians: Modeling dynamic urban scenes with gaussian splatting.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.", "was_revised": true, "final_justification": "After reading the rebuttal, I would like to keep my score.", "rebuttal": "Thank you for your valuable time and comments.  Because the official prohibits the use of any links, we are unable to update the visualization. We hope you can understand. We will update the new visualization in the final version. I think my following reply will alleviate your concerns:\n\n**W1: Better quality visualization.** Thank you for your suggestion. Our fellow STORM maintains an output resolution of 160*240, so the quality seems relatively low. Our algorithm supports higher resolutions, and our further experimental results show that with higher resolution supervision, our results will be better. We will incorporate better new perspective visualizations (FreeVS, StreetCrafter, and ReconDreamer) in the final version.\n\n**W2: Scene editing tasks.** Our model supports Dong Jiangtao's separation. So we can add any object to the static GS background. Besides, we can also control the object to move and rotate. We will incorporate our visualization in the final version.\n\n**W3: The reason of low quality per-scene optimization methods.**  Fellow STORM, for a fair comparison of the feed-forward method, there are three changes here: (1) We did not use point cloud initialization. (2) We used fewer perspectives for supervision (4 timestep * 3 views). Such a mechanism has led to the deterioration of the per-scene optimization results.\n\n**Q1: The details of the formula.**  Depth categories is where we use the classification header to predict the depth value. Here, we use a total of 256 categories from 0 to 256 to represent the range from 0 to 256 meters. L_dr and L_sr respectively represent the rendering supervision of dynamic objects and static objects. L_dr and L_dy are the same thing. We will make corrections in the final version.\n\nWe once again apologize for the inability to update the visualization. We will provide higher resolution and more specific new perspectives (+ 1m, +2m, +3m) and other visualization results in the final version."}, {"review_id": "ylX5DuPcy5", "reviewer": "Reviewer_8TUk", "summary": "This paper proposes a Large 4D Gaussian Reconstruction Model for autonomous driving spatial-temporal model reconstruction. There are two main contributions: 1) a prune and dilate module to reduce the number of Gaussian and 2) static and dynamic objects are decoupled to improve the reconstruction quality. Experiments show that the proposed method outperforms state-of-the-art methods on the nuScenes dataset.", "questions": "The rendered resolution of your model remains low: what are the main concerns regarding a higher resolution with 3DGS?\n\nThe loss function is composed by several heterogeneous losses: how do you optimize the values of associated hyperparamaters?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "++\nThe contributions are clearly stated\nExperiments show that the proposed method outperforms state-of-the-art methods on the nuScenes dataset.\nThe related work section includes most of the recent relevant works in the field of Large Reconstruction Models and Driving Scene Reconstruction.\n\n--\nThe main issue with the paper is that it lacks some details in the method section: There is no details about the image encoder (size, architecture, etc.). Moreover, the image decoder is not described in detail, which makes it difficult to understand how the image features are transformed into Gaussian points. Moreover, the main contribution of the paper: Prune and Dilate Module is not described in detail: what are the hyperparameters, how they are chosen, etc. In the Static and Dynamic Object Decoupling section, we suppose that the dynamic objects are selected based on their class labels, but this is not clearly stated in the paper.", "was_revised": true, "final_justification": "Even if the paper is still having some issues, I think that the authors have addressed most of my concerns. I think that the proposed model may by accepted if possible.", "rebuttal": "Thank you, reviewers, for your valuable time and comments. Your biggest concern is the model details, the PD-Block details and the segmentation details. I think my following reply will alleviate your concerns:\n\n**W1: The details about the model.** Our Encoder and Decoder are of UNet structure. The U-Net is built with residual layers and self-attention layers similar to previous works [1, 2].\nAfter UNet, GS Adapter is connected as shown in lines 134-139.\n\n**W2: The details about the PD-Block.** The details of PD-Block are in the supplementary materials. We conducted ablation experiments on the three most important parameters and found that our algorithm is robust to the parameters.\n\n| Parameters | PSNR  | SSIM  | RMSE |\n|------------|-------|-------|------|\n| base       | 23.70 | 0.590 | 8.92 |\n| τ (0.1)    | 24.10 | 0.620 | 8.50 |\n| τ (0.3)    | 25.22 | 0.643 | 7.62 |\n| τ (0.5)    | 25.50 | 0.640 | 7.80 |\n| τ (0.7)    | 25.44 | 0.642 | 7.77 |\n| τ (0.9)    | 24.85 | 0.610 | 8.35 |\n| K=3, Z=9   | 25.00 | 0.662 | 7.03 | \n| K=5, Z=25  | 25.80 | 0.645 | 7.50 |\n| K=7, Z=49  | 25.40 | 0.630 | 7.95 |\n\n**W3: The details about the dynamic objects.**  The way of selecting dynamic objects to supplement materials. We filter dynamic objects through the speed of boxes, and then use boxes as prompts to segment dynamic objects with SAM.\n\n[1] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. NeurIPS 33,\n6840–6851 (2020)\n\n[2] Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour,\nK., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic textto-image diffusion models with deep language understanding. NeurIPS 35, 36479–\n36494 (2022)"}, {"review_id": "asIrJp1WXG", "reviewer": "Reviewer_WHfH", "summary": "This paper proposes DrivingRecon, a feed-forward 4D Gaussian scene reconstruction model for autonomous driving. It predicts dynamic and static Gaussians from multi-view video using a shared 2D encoder and a range-view decoder. A core contribution is the Prune and Dilate Block (PD-Block), which uses a data-driven learning pattern for spatial redundancy by pruning overlapping Gaussians and reallocating them to complex regions. The work demonstrates support for downstream tasks such as planning, scene editing, and novel view synthesis, and achieves strong performance on nuScenes and Waymo datasets. The system is trained at scale and claims real-time rendering speeds, however, it involves hand-crafted thresholds and omits comparison to recent real-time online GS reconstruction methods that do not require large model training, raising questions about scalability and fairness in the reported claims.", "questions": "I wrote my questions in the weaknesses section above.", "limitations": "Yes", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The PD-Block is a technically sound idea to mitigate spatial redundancy and reallocate Gaussians.\n2.The paper reports strong performance on benchmark datasets and supports multiple downstream tasks (reconstruction, editing). \n3. The framework is feed-forward and generalizable, avoiding per-scene optimization and enabling real-time rendering.\n\nWeaknesses:\n1. For the Inference speed reported in Table 1, is it the average time to run all frames of different scenes (whole scene)? Or it is like, it is the total optimization time for the per-scene optimization methods, but it is the per-frame inference time for generalizable feed-forward methods? So DriveRecon's 0.08s is per-frame inference speed including the regression of all attributes (including 2D/3D flow and semantics) of all Gaussians?\n2. I also doubt if it is fair and appropriate to compare the \"Inference Speed\" this way in Table 1. For the \"Per-Scene Optimization\" methods, the time is not just for inference, it also includes training. The proposed DriveRecon trained generalization with extremely huge computation (24 x A100 x 24days), so I think it is unfair and inappropriate to compare the \"Inference Speed\" in this way which includes the optimization time of the \"per-scene optimization\" methods. \n3. Following the comments above, I think the generalizable model is not necessary if per-scene optimization is fast enough, and the generalizable model is too difficult to train. There are already some real-time online GS reconstruction method since last year, such as RTGSLAM (https://github.com/MisEty/RTG-SLAM/tree/main) which achieves real-time GS reconstruction without the need for pretraining. I think a better comparison for Table 1 would be including these real-time GS reconstruction methods, which could potentially achieve much faster inference speed than the \"generalizable ones\". Plus, some methods such as RTGSLAM also proposes GS management algorithm to efficiently grow and prune GS - If they work comparably good then there is no point of taking huge hardware and time resources to train the model. This is why I think the comparison to these works are very important.\n4. Writing and formatting issues: Please check the paper contents carefully. There are mismatches in the explanation of terms around line 98–99, and the order of term definitions is also messy. Additionally, typos such as \"fellowing\" (lines 181, 189) instead of \"following\" suggest the writing needs more careful proofreading before submission.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you, reviewers, for your valuable time and comments. Your biggest concerns are the reasoning speed and training time. I think my following reply will alleviate your concerns:\n\n**W1: Inference speed reported in Table 1.**  Our inference speed (0.8s) includes the entire scenario (spaced at t + 0s, t + 0.5s, t + 1.0s, and t + 1.5s) and include all Gaussian properties (including 2D/3D flow and semantics). This setting is exactly the same as that of STORM and is a fair comparison.\n\n**W2: Fairness in the comparison of reasoning speeds.**  Our algorithm only requires 24 gpu days in total (24 A100 * 1 days) instead of (24 x A100 x 24days). Furthermore, our experiment found that it was already close to the best performance at 2 gpu days (24 A100 * 2 hours).\n\n**W3: Compared with RTGSLAM.** RTGSLAM requires a dense depth map as input. RTGSLAM still takes time to predict dense depth values through RGB images. For comparison, our algorithm only requires RGB images as input to reconstruct 4D Gaussian. Our method has already surpassed the existing Feedforward in the driving field in terms of speed and quality indicators. Unlike Gaussian through post-processing (grow and prune), our algorithm is learnable and end-to-end.\n\n**W4: Writing and formatting issues.** We will correct these printing errors and typesetting in the final version."}]

Author rebuttal:

Meta-review: The paper presented DrivingRecon, a feed-forward model for 4D scene reconstruction in autonomous driving. By using a shared 2D encoder and range-view decoder, the model efficiently fuses multi-view images to generate 4D Gaussian representations. The core innovations are 1) the Prune and Dilate Block (PD-Block) to prune redundant and overlapping Gaussian points and dilate Gaussian points for complex objects. 2) dynamic and static decoupling to better learn the temporary consistent geometry across different time.

It was reviewed by four reviewers in the community. Initially, it received mixed ratings as 1XAccept, 2XBorderline Accept and 1XReject. The major weaknesses lie in Inadequate Validation for Fast-Moving or Small Dynamic Objects,  Adaptive Limitations of PD-Block, Computational Scalability, Temporal Consistency in Long Sequences, Scene editing tasks, missing details,  The authors submitted rebuttal to address concerns from the reviewers, which as the reviewers confirmed resolved most of the comments. Reviewer WHfH did not respond during the discussion period. 

Given the consistency, I would like to accept the paper. The authors are requested to revise the paper accordingly.
Decision: Accept (poster)