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REVIEW 3 major objections 6 minor 1 cited by

Extrapolated Urban View Synthesis Benchmark

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Current urban view-synthesis models overfit their training views, and a new extrapolated-view benchmark exposes the failure.

desk verdict Useful benchmark for extrapolated urban NVS, but the 'extrapolation' is weaker than claimed because test views are manually selected to be well covered by training trajectories. read the letter →

arxiv 2412.05256 v3 pith:D3RSFSSV submitted 2024-12-06 cs.CV cs.AIcs.LGcs.RO

Xiangyu Han , Zhen Jia , Boyi Li , Yan Wang , Boris Ivanovic , Yurong You , Lingjie Liu , Yue Wang
show 3 more authors
Marco Pavone Chen Feng Yiming Li
This is my paper · ORCID
classification cs.CVcs.AIcs.LGcs.RO
keywords extrapolatedviewsynthesisnovel3DGaussianSplattingautonomousdrivingsimulationurbanscenesradiancefieldsmulti-traversaldatabenchmark
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces the first benchmark built to measure how well novel-view-synthesis models render urban driving scenes from viewpoints that lie outside the training set. The benchmark assembles 90,810 frames from public autonomous-driving recordings that revisit the same places from different lanes, different vehicles, and different cameras, then splits them into interpolated and extrapolated test views across three settings: translation only, rotation only, and translation plus rotation. Across every tested method, rendering quality falls sharply when the test viewpoint extrapolates, while interpolation metrics stay close to training values. The paper reads this as evidence that current scene-fitting radiance-field methods overfit their training views. The benchmark matters because autonomous-vehicle simulators need exactly this ability to generate new viewpoints, not just nearby ones.

What carries the argument

The load-bearing mechanism is the benchmark's train/test construction. Instead of sampling test cameras near training cameras, it draws them from separate traversals, agents, or cameras that visit the same scene from shifted poses, so the evaluation measures extrapolation rather than interpolation. Three settings isolate the type of pose change: pure translation, pure rotation, and combined translation plus rotation. Sparse structure-from-motion points initialize all Gaussian methods, movable objects are masked out of training and evaluation, and a suite of image-quality, perceptual, feature-similarity, and depth metrics records the drop. A per-image appearance model is included as a control, showing that part of the extrapolation gap can be absorbed by lighting compensation but a large residual remains.

What would settle it

Render the same extrapolated test poses while holding appearance constant, for example by taking multiple passes over the same route under matched lighting or by applying a strong per-image appearance model, and compare the metric drop. If the interpolation-to-extrapolation gap shrinks to near zero under matched appearance, the benchmark would be measuring appearance shift rather than pose extrapolation; if the gap persists, the overfitting-to-training-views claim is supported.

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Extended reading notes

Core claim

The central discovery is that contemporary radiance-field methods, especially 3D Gaussian Splatting, do not generalize to extrapolated urban viewpoints. On the benchmark, vanilla 3D Gaussian Splatting loses 23.4% PSNR in the translation-only setting, 24.2% in the rotation-only setting, and 29.4% when both translation and rotation change; average PSNR drops across all baselines reach 24.6%, 25.6%, and 30.6% respectively. Perceptual similarity degrades even more sharply, with learned perceptual error rising by over 170% for one baseline in the hardest setting. Methods that add diffusion priors, planar geometry, or depth regularization improve isolated cases but do not close the gap, and a per-image appearance model raises absolute quality while leaving a large interpolation-to-extrapolation drop. The paper concludes that the bottleneck is structural: fitted scenes lack the geometry and appearance coverage needed for views that deviate substantially from training trajectories.

Load-bearing premise

The load-bearing premise is that the test views are true pose extrapolations and that the measured quality drop comes from novel viewpoint geometry rather than from lighting, weather, or time-of-day differences between the recording traversals.

Editorial extensions

If this is right

  • Researchers can now compare extrapolated view-synthesis methods on a fixed protocol with ground-truth images instead of relying on qualitative demos.
  • Any method that claims to generalize for driving simulation should be expected to report extrapolated urban view-synthesis metrics, because interpolation scores can remain high while true generalization fails.
  • Adding diffusion priors or geometric regularization is not sufficient by itself; the benchmark's results suggest the community needs representations trained or regularized explicitly for unseen viewpoints.
  • More traversal data helps: increasing the number of traversals improves extrapolated metrics up to a plateau, so data diversity is a measurable lever.
  • Dynamic-scene reconstruction methods show the same failure, so the overfitting pattern is not limited to static background models.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If missing geometry rather than missing appearance drives the failure, then scaling the number of distinct viewpoints per scene may matter more than adding generative priors, and the plateau seen when adding traversals hints at an upper bound from current per-scene fitting.
  • A natural next experiment is to train a feed-forward generalizable model on many EUVS scenes and test zero-shot extrapolation; the benchmark's static-scene protocol would make that comparison straightforward.
  • The benchmark's residual gap after appearance compensation suggests future versions should record lighting and weather metadata per traversal, letting the community separate pose extrapolation from appearance shift instead of controlling only by manual curation.
  • Extending the evaluation to foreground objects in the translation and combined settings, using multi-agent views of the same dynamic objects, would test whether per-object appearance models can rescue extrapolated dynamic scenes.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper introduces EUVS, a benchmark for evaluating novel view synthesis (NVS) under extrapolated viewpoints in urban driving scenes. The authors assemble data from nuPlan, Argoverse 2, and MARS, exploiting multi-traversal, multi-agent, and multi-camera recordings to define three evaluation settings: translation-only, rotation-only, and translation+rotation. They train several state-of-the-art Gaussian Splatting and NeRF methods and report a consistent, substantial drop in rendering metrics (PSNR, SSIM, LPIPS, feature cosine similarity) when moving from interpolated to extrapolated test views, concluding that current methods overfit to training views and that diffusion priors or geometry refinement do not fundamentally solve the problem. The paper also includes perceptual (FID) and depth evaluations, a dynamic-scene baseline, and a per-image appearance model (GS-W) to mitigate lighting inconsistencies.

Significance. If the extrapolation claim is valid, this is a valuable contribution: it addresses a real gap in quantitative evaluation for a capability that matters for AV simulators, and the multi-traversal/multi-agent data curation is nontrivial. The breadth of baselines (3DGS, 3DGM, GaussianPro, VEGS, PGSR, 2DGS, Feature 3DGS, Zip-NeRF, Instant-NGP, GS-W, OmniRe) and the plan to release data and code are strengths. However, the central finding is only as strong as the definition of extrapolation, and the manuscript currently contains statements that undercut that definition; the lack of a quantitative pose-gap measure and the potential appearance confound are load-bearing concerns. If those are resolved, the benchmark would be a useful community resource.

major comments (3)
  1. [Sec. 6 and Sec. 4.2] Section 6 (Conclusions, Limitations and Future Work) states: "we carefully manually selected and ensured that the test trajectory viewpoints are well covered by training trajectories," and Section 4.2, Setting 1, states: "training views fully cover test views with moderate translational changes." These statements contradict the abstract's characterization of extrapolation as test views that "largely deviate from training views." The manuscript does not provide a quantitative definition of extrapolation (e.g., nearest-neighbor camera-pose distance, fraction of test views outside the training pose distribution, or angular/translational thresholds). As a result, the reported drops in Table 1 (e.g., 3DGS PSNR 21.36 to 16.37 in Setting 1) cannot be attributed to viewpoint extrapolation; they may reflect the authors' coverage-selection choice. The authors should add a quantitative extrapolation metric and demonstrate that test views lie outside a defined extrapolation regime, or substantially revise the claims and title to describe sparsely covered novel-view synthesis.
  2. [Sec. 5 and Table 4] Section 5 (Lighting Inconsistency Handling) and Table 4 show that per-image appearance embeddings (GS-W) produce large absolute gains: extrapolated PSNR rises from 16.37 to 20.22 in Setting 1 and from 14.99 to 19.36 in Setting 3, bringing the extrapolated numbers close to the vanilla 3DGS interpolated values. This indicates that appearance variation across traversals is a substantial confound for the extrapolation comparison. The manual curation "to the eye" is not a quantitative control. The authors should quantify appearance shift (e.g., mean color statistics or a learned appearance-distance measure) between training and test traversals, and ideally provide an appearance-controlled comparison (e.g., GS-W with and without extrapolated poses) to isolate the geometric extrapolation effect from appearance differences.
  3. [Sec. 4.1] Section 4.1 states that Grounded-SAM-2 is used to mask potentially movable objects in both training and evaluation, so the reported metrics are computed on static background only. This is disclosed, but the paper does not quantify the masked fraction or discuss how the masking affects the comparison across methods. The authors should report the average masked area and state explicitly that conclusions about "overfitting to training views" apply to static scene content; the current wording in the abstract could be read as applying to full scenes.
minor comments (6)
  1. [Sec. 2] In the second paragraph of Related Works, the sentence "a lack of real data for quantitative evaluation, which confines them to quantitative analysis" should read "qualitative analysis".
  2. [Table 1] The header "A VERAGE" should be "AVERAGE".
  3. [Sec. 4.2, Setting 2] The text states "PSNR dropping by 22.75%," but the numbers in Table 1 show an average drop of 25.6% and a 3DGS drop of 24.2%; please reconcile the stated value.
  4. [Table 2 caption] The caption misspells "inpainting ability" as "impainting ability."
  5. [Figure 1] The percentage labels (77%, 79%, etc.) are not explained; please clarify which metrics they correspond to.
  6. [Table 4] For direct comparability with Table 1, consider reporting the interpolation-to-extrapolation drop percentages for GS-W in each setting.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark measures external NVS methods against standard metrics on public datasets; the coverage limitation affects validity, not circularity.

full rationale

The paper's central claim is an empirical evaluation of existing novel view synthesis methods on extrapolated urban views. The reported PSNR, SSIM, LPIPS, depth, and feature-similarity drops are measurements of external baselines (3DGS, 3DGM, GSPro, VEGS, PGSR, 2DGS, Feature 3DGS, Zip-NeRF, Instant-NGP, GS-W, OmniRe) under the authors' benchmark protocol. No parameter is fitted to a subset of data and then presented as a prediction of a closely related quantity; no equation reduces an output metric to an input fit by construction. The benchmark uses publicly available datasets (nuPlan, Argoverse 2, MARS) and standard evaluation metrics, and the paper's own qualitative and quantitative comparisons are directly tied to external ground-truth images. Self-citations appear (e.g., MARS dataset and the 3DGM baseline), but they are not load-bearing: MARS is a public dataset and 3DGM is simply one evaluated baseline, not the justification for the paper's conclusions. The limitation section's statement that test trajectory viewpoints were 'well covered by training trajectories' is a substantive concern about whether the tested condition matches the definition of extrapolation, and it may weaken the strength of the empirical claim about large-view extrapolation. However, that is a construct-validity issue, not circular reasoning: the observed performance drops are empirical outcomes and are not forced by the benchmark definition or by any fitted parameter. The paper is therefore self-contained with respect to circularity; the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The benchmark itself is not a physical entity; the axioms above are the unverified premises the evaluation rests on.

assumptions (5)
  • domain assumption Public AV datasets (nuPlan, Argoverse 2, MARS) provide sufficiently accurate poses and images for view synthesis evaluation.
    The benchmark relies on these datasets for ground truth; pose errors or synchronization issues would affect all evaluated methods.
  • domain assumption COLMAP sparse reconstruction provides reliable camera poses and initialization for all scenes.
    All 3DGS methods are initialized from COLMAP points only; if COLMAP poses are inaccurate for large-baseline multi-traversal alignment, the comparison is unfair or invalid.
  • domain assumption Grounded-SAM-2 masks all potentially movable objects correctly.
    Dynamic objects are masked out during training and evaluation; incorrect masks would introduce errors in static-scene metrics.
  • ad hoc to paper The manually selected test trajectories are extrapolated even though they are well covered by training views.
    The paper selects cases where test viewpoints are well covered by training trajectories (Section 6), which is a conflicting criterion for extrapolation and may understate the true difficulty.
  • domain assumption Performance differences between interpolated and extrapolated views are due to viewpoint extrapolation, not appearance shifts.
    Multi-traversal data is captured at different times, so lighting and weather differ; the paper manually curates for visual consistency but does not quantitatively control for appearance, and GS-W experiments show appearance modeling matters. This confound could inflate the reported extrapolation drops.

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Cite this review

Pith. "Pith review of Extrapolated Urban View Synthesis Benchmark." pith.science (2026). https://pith.science/paper/D3RSFSSV

@misc{pith2026241205256,
  author       = {Pith},
  title        = {Pith review of: Extrapolated Urban View Synthesis Benchmark},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D3RSFSSV}},
  note         = {Machine review of arXiv:2412.05256}
}
read the original abstract

Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs). At their core is Novel View Synthesis (NVS), a crucial capability that generates diverse unseen viewpoints to accommodate the broad and continuous pose distribution of AVs. Recent advances in radiance fields, such as 3D Gaussian Splatting, achieve photorealistic rendering at real-time speeds and have been widely used in modeling large-scale driving scenes. However, their performance is commonly evaluated using an interpolated setup with highly correlated training and test views. In contrast, extrapolation, where test views largely deviate from training views, remains underexplored, limiting progress in generalizable simulation technology. To address this gap, we leverage publicly available AV datasets with multiple traversals, multiple vehicles, and multiple cameras to build the first Extrapolated Urban View Synthesis (EUVS) benchmark. Meanwhile, we conduct both quantitative and qualitative evaluations of state-of-the-art NVS methods across different evaluation settings. Our results show that current NVS methods are prone to overfitting to training views. Besides, incorporating diffusion priors and improving geometry cannot fundamentally improve NVS under large view changes, highlighting the need for more robust approaches and large-scale training. We will release the data to help advance self-driving and urban robotics simulation technology.

Figures

Figures reproduced from arXiv: 2412.05256 by the authors.

Figure 1
Figure 1. Our key contributions. Previous evaluations for urban view synthesis have primarily focused on interpolated poses, as the lack of ground truth data has made it challenging to evaluate extrapolated poses. We address this gap by providing real-world data that enables both quantitative and qualitative evaluations of extrapolated view synthesis in urban scenes. The quantitative results reveal a significant performance d… view at source ↗
Figure 2
Figure 2. Dataset visualization. Our dataset features diverse scenes across various locations in different cities, sourced from multiple datasets. Typical driving scenarios include maneuvers such as lane changes, cross intersections, and T-junctions. Top: Each column displays images captured at the same location by different agents or traversals. Bottom: Each image displays the COLMAP points at a specific location, along with… view at source ↗
Figure 3
Figure 3. Dataset distribution. Our dataset comprises 90,810 frames distributed over 104 cases, capturing a diverse array of multi-traversal paths, multi-agent interactions, and multi-camera perspectives across varying evaluation settings. scenarios where the vehicle’s position shifts without any change in orientation. This scenario is commonly observed in lane changes. We use traversals from different lanes in multi-traversa… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Qualitative and quantitative results across three evaluation settings. The performance drop from interpolation to extrapola￾tion is significant in both qualitative and quantitative comparison. Different testing settings have distinct scenario characteristics, enabling …
Figure 5
Figure 5. Figure 5: Qualitative comparison of extrapolated view synthesis across different settings. For each setting, results from different methods are compared against the ground truth. Red boxes highlight areas where methods are limited in capturing fine details, such as road surfaces…
Figure 6
Figure 6. Figure 6: Qualitative results of dynamic baseline and lighting handling. We mitigate the lighting inconsistency issue by carefully selecting traversals under similar lighting conditions, and it can be further alleviated by introducing camera embeddings. We provide the quantitati…

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Reference graph

Works this paper leans on

68 extracted references · 41 canonical work pages · cited by 1 Pith paper

  1. [1]

    Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles

    Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski, Wilko Schwarting, Zhijian Liu, Song Han, Sertac Karaman, and Daniela Rus. Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles. In 2022 International Conference on Robotics and Automation (ICRA), pages 2419–2426. IEEE, 2022. 3

    work page 2022

  2. [2]

    Zip-nerf: Anti-aliased grid-based neural radiance fields

    Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Zip-nerf: Anti-aliased grid-based neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19697–19705, 2023. 5, 6, 1

    work page 2023

  3. [3]

    nuscenes: A multi- modal dataset for autonomous driving

    Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom. nuscenes: A multi- modal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11621–11631, 2020. 3

    2020

  4. [4]

    nuplan: A closed-loop ml-based plan- ning benchmark for autonomous vehicles

    Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric Wolff, Alex Lang, Luke Fletcher, Oscar Beijbom, and Sammy Omari. nuplan: A closed-loop ml-based plan- ning benchmark for autonomous vehicles. arXiv preprint arXiv:2106.11810, 2021. 2, 3, 4

    arXiv 2021

  5. [5]

    Argoverse: 3d tracking and forecasting with rich maps

    Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jag- jeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, et al. Argoverse: 3d tracking and forecasting with rich maps. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8748–8757, 2019. 3

    work page 2019

  6. [6]

    pixelsplat: 3d gaussian splats from im- age pairs for scalable generalizable 3d reconstruction

    David Charatan, Sizhe Lester Li, Andrea Tagliasacchi, and Vincent Sitzmann. pixelsplat: 3d gaussian splats from im- age pairs for scalable generalizable 3d reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19457–19467, 2024. 2

    work page 2024

  7. [7]

    Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction

    Danpeng Chen, Hai Li, Weicai Ye, Yifan Wang, Weijian Xie, Shangjin Zhai, Nan Wang, Haomin Liu, Hujun Bao, and Guofeng Zhang. Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction. arXiv preprint arXiv:2406.06521, 2024. 5, 6, 7, 1

    arXiv 2024

  8. [8]

    Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering

    Yurui Chen, Chun Gu, Junzhe Jiang, Xiatian Zhu, and Li Zhang. Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering. arXiv preprint arXiv:2311.18561, 2023. 2, 3

    arXiv 2023

Show all 68 references
  1. [9]

    Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images

    Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images. arXiv preprint arXiv:2403.14627, 2024. 2

    2024 arXiv

  2. [10]

    Omnire: Omni ur- ban scene reconstruction

    Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Go- jcic, Sanja Fidler, Marco Pavone, et al. Omnire: Omni ur- ban scene reconstruction. arXiv preprint arXiv:2408.16760,

    arXiv

  3. [11]

    Gaussian- pro: 3d gaussian splatting with progressive propagation

    Kai Cheng, Xiaoxiao Long, Kaizhi Yang, Yao Yao, Wei Yin, Yuexin Ma, Wenping Wang, and Xuejin Chen. Gaussian- pro: 3d gaussian splatting with progressive propagation. In Forty-first International Conference on Machine Learning,

  4. [12]

    Depth-regularized optimization for 3d gaussian splatting in few-shot images

    Jaeyoung Chung, Jeongtaek Oh, and Kyoung Mu Lee. Depth-regularized optimization for 3d gaussian splatting in few-shot images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 811–820, 2024. 1

    2024

  5. [13]

    Ithaca365: Dataset and driving perception under repeated and challenging weather conditions

    Carlos A Diaz-Ruiz, Youya Xia, Yurong You, Jose Nino, Junan Chen, Josephine Monica, Xiangyu Chen, Katie Luo, Yan Wang, Marc Emond, et al. Ithaca365: Dataset and driving perception under repeated and challenging weather conditions. In Proceedings of the IEEE/CVF Conference on C...

    2022

  6. [14]

    Carla: An open urban driv- ing simulator

    Alexey Dosovitskiy, German Ros, Felipe Codevilla, Anto- nio Lopez, and Vladlen Koltun. Carla: An open urban driv- ing simulator. In Conference on robot learning, pages 1–16. PMLR, 2017. 2, 3

    2017

  7. [15]

    Large scale interactive motion forecasting for autonomous driving: The waymo open mo- tion dataset

    Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Ben Sapp, Charles R Qi, Yin Zhou, et al. Large scale interactive motion forecasting for autonomous driving: The waymo open mo- tion dataset. In Proceedings of the IEEE/CVF Intern...

    2021

  8. [16]

    Freesim: Toward free-viewpoint camera simu- lation in driving scenes, 2024

    Lue Fan, Hao Zhang, Qitai Wang, Hongsheng Li, and Zhaox- iang Zhang. Freesim: Toward free-viewpoint camera simu- lation in driving scenes, 2024. 3

    2024

  9. [17]

    Trafficgen: Learning to generate diverse and realistic traffic scenarios

    Lan Feng, Quanyi Li, Zhenghao Peng, Shuhan Tan, and Bolei Zhou. Trafficgen: Learning to generate diverse and realistic traffic scenarios. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 3567–3575. IEEE, 2023. 3

    2023

  10. [18]

    A new performance measure and evaluation benchmark for road de- tection algorithms

    Jannik Fritsch, Tobias Kuehnl, and Andreas Geiger. A new performance measure and evaluation benchmark for road de- tection algorithms. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pages 1693–1700. IEEE, 2013. 3

    2013

  11. [19]

    Vista: A generalizable driving world model with high fidelity and versatile controllability

    Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, and Hongyang Li. Vista: A generalizable driving world model with high fidelity and versatile controllability. arXiv preprint arXiv:2405.17398, 2024. 3

    2024 arXiv

  12. [20]

    Are we ready for autonomous driving? the kitti vision benchmark suite

    Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, pages 3354–3361. IEEE, 2012. 3

    2012

  13. [21]

    Ggs: Generalizable gaussian splatting for lane switching in autonomous driving

    Huasong Han, Kaixuan Zhou, Xiaoxiao Long, Yusen Wang, and Chunxia Xiao. Ggs: Generalizable gaussian splatting for lane switching in autonomous driving. arXiv preprint arXiv:2409.02382, 2024. 2, 3

    2024 arXiv

  14. [22]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. Advances in neural information processing systems, 30, 2017. 6

    2017

  15. [23]

    2d gaussian splatting for geometrically accu- rate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accu- rate radiance fields. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 3, 5, 6, 1

    2024

  16. [24]

    Vegs: View extrapolation of urban scenes in 3d gaussian splatting using learned priors

    Sungwon Hwang, Min-Jung Kim, Taewoong Kang, Jayeon Kang, and Jaegul Choo. Vegs: View extrapolation of urban scenes in 3d gaussian splatting using learned priors. arXiv preprint arXiv:2407.02945, 2024. 2, 3, 5, 6, 7, 1

    2024 arXiv

  17. [25]

    Gaussianshader: 3d gaussian splatting with shading functions for reflective surfaces

    Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaox- iao Long, Wenping Wang, and Yuexin Ma. Gaussianshader: 3d gaussian splatting with shading functions for reflective surfaces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 53...

    2024

  18. [26]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 1, 2, 3, 4, 5, 6, 7

    2023

  19. [27]

    Autosplat: Constrained gaussian splatting for autonomous driving scene reconstruction

    Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma, Tongtong Cao, Dongfeng Bai, Yuan Ren, and Bingbing Liu. Autosplat: Constrained gaussian splatting for autonomous driving scene reconstruction. arXiv preprint arXiv:2407.02598, 2024. 3

    2024 arXiv

  20. [28]

    Drivegan: Towards a controllable high- quality neural simulation

    Seung Wook Kim, Jonah Philion, Antonio Torralba, and Sanja Fidler. Drivegan: Towards a controllable high- quality neural simulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5820–5829, 2021. 3

    2021

  21. [29]

    Scenarionet: Open-source platform for large-scale traffic scenario sim- ulation and modeling

    Quanyi Li, Zhenghao Mark Peng, Lan Feng, Zhizheng Liu, Chenda Duan, Wenjie Mo, and Bolei Zhou. Scenarionet: Open-source platform for large-scale traffic scenario sim- ulation and modeling. Advances in neural information processing systems, 36, 2024. 3

    2024

  22. [30]

    V2x-sim: Multi-agent col- laborative perception dataset and benchmark for autonomous driving

    Yiming Li, Dekun Ma, Ziyan An, Zixun Wang, Yiqi Zhong, Siheng Chen, and Chen Feng. V2x-sim: Multi-agent col- laborative perception dataset and benchmark for autonomous driving. IEEE Robotics and Automation Letters, 7(4): 10914–10921, 2022. 3

    2022

  23. [31]

    Multiagent multitraversal multimodal self-driving: Open mars dataset

    Yiming Li, Zhiheng Li, Nuo Chen, Moonjun Gong, Zonglin Lyu, Zehong Wang, Peili Jiang, and Chen Feng. Multiagent multitraversal multimodal self-driving: Open mars dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22041–22051, 2...

    2024

  24. [32]

    Yiming Li, Zehong Wang, Yue Wang, Zhiding Yu, Zan Goj- cic, Marco Pavone, Chen Feng, and Jose M. Alvarez. Memo- rize what matters: Emergent scene decomposition from mul- titraverse. In Advances in Neural Information Processing Systems (NeurIPS), 2024. 4, 6, 7, 1

    2024

  25. [33]

    Matthew Loper, Naureen Mahmood, Javier Romero, Ger- ard Pons-Moll, and Michael J. Black. SMPL: A skinned multi-person linear model. ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1–248:16, 2015. 8

    2015

  26. [34]

    Object scene flow for au- tonomous vehicles

    Moritz Menze and Andreas Geiger. Object scene flow for au- tonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061– 3070, 2015. 3

    2015

  27. [35]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM, 65(1):99–106, 2021. 3

    2021

  28. [36]

    Instant neural graphics primitives with a multires- olution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a multires- olution hash encoding. ACM Trans. Graph., 41(4):102:1– 102:15, 2022. 5, 6

    2022

  29. [37]

    Dinov2: Learning robust visual features without supervision

    Maxime Oquab, Timoth ´ee Darcet, Th ´eo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 6

    2023 arXiv

  30. [38]

    Canadian adverse driving conditions dataset

    Matthew Pitropov, Danson Evan Garcia, Jason Rebello, Michael Smart, Carlos Wang, Krzysztof Czarnecki, and Steven Waslander. Canadian adverse driving conditions dataset. The International Journal of Robotics Research, 40 (4-5):681–690, 2021. 3

    2021

  31. [39]

    Sam 2: Segment anything in images and videos,

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R¨adle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junt- ing Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Doll´ar, and Christoph Feic...

  32. [40]

    Structure-from-motion revisited

    Johannes Lutz Sch ¨onberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2, 6

    2016

  33. [41]

    Airsim: High-fidelity visual and physical simula- tion for autonomous vehicles

    Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. Airsim: High-fidelity visual and physical simula- tion for autonomous vehicles. In Field and Service Robotics: Results of the 11th International Conference, pages 621–635. Springer, 2018. 3

    2018

  34. [42]

    Dhgs: Decoupled hybrid gaussian splatting for driving scene.arXiv preprint arXiv:2407.16600,

    Xi Shi, Lingli Chen, Peng Wei, Xi Wu, Tian Jiang, Yonggang Luo, and Lecheng Xie. Dhgs: Decoupled hybrid gaussian splatting for driving scene.arXiv preprint arXiv:2407.16600,

    arXiv

  35. [43]

    Scenegen: Learning to generate realistic traffic scenes

    Shuhan Tan, Kelvin Wong, Shenlong Wang, Sivabalan Mani- vasagam, Mengye Ren, and Raquel Urtasun. Scenegen: Learning to generate realistic traffic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 892–901, 2021. 3

    2021

  36. [44]

    Vision in ad- verse weather: Augmentation using cyclegans with various object detectors for robust perception in autonomous racing

    Izzeddin Teeti, Valentina Musat, Salman Khan, Alexander Rast, Fabio Cuzzolin, and Andrew Bradley. Vision in ad- verse weather: Augmentation using cyclegans with various object detectors for robust perception in autonomous racing. arXiv preprint arXiv:2201.03246, v3, 2023. 3

    2023 arXiv

  37. [45]

    Neurad: Neural rendering for autonomous driving

    Adam Tonderski, Carl Lindstr ¨om, Georg Hess, William Ljungbergh, Lennart Svensson, and Christoffer Petersson. Neurad: Neural rendering for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14895–14904, 2024. 2

    2024

  38. [46]

    latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction

    Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, and Jan Eric Lenssen. latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction. arXiv preprint arXiv:2403.16292, 2024. 2

    2024 arXiv

  39. [47]

    Argoverse 2: Next generation datasets for self-driving perception and forecasting

    Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, et al. Argoverse 2: Next generation datasets for self-driving perception and forecasting. arXiv preprint arXiv:230...

    2023 arXiv

  40. [48]

    Mapnerf: Incorporating map priors into neural ra- diance fields for driving view simulation

    Chenming Wu, Jiadai Sun, Zhelun Shen, and Liangjun Zhang. Mapnerf: Incorporating map priors into neural ra- diance fields for driving view simulation. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7082–7088. IEEE, 2023. 3

    2023

  41. [49]

    Hgs-mapping: Online dense mapping using hybrid gaussian representation in urban scenes

    Ke Wu, Kaizhao Zhang, Zhiwei Zhang, Shanshuai Yuan, Muer Tie, Julong Wei, Zijun Xu, Jieru Zhao, Zhongxue Gan, and Wenchao Ding. Hgs-mapping: Online dense mapping using hybrid gaussian representation in urban scenes. arXiv preprint arXiv:2403.20159, 2024. 3

    2024 arXiv

  42. [50]

    Mars: An instance-aware, modu- lar and realistic simulator for autonomous driving

    Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuantao Chen, Runyi Yang, et al. Mars: An instance-aware, modu- lar and realistic simulator for autonomous driving. In CAAI International Conference on Artificial Intelligence, pages 3–

  43. [51]

    Torcs, the open racing car simulator

    Bernhard Wymann, Eric Espi ´e, Christophe Guionneau, Christos Dimitrakakis, R ´emi Coulom, and Andrew Sumner. Torcs, the open racing car simulator. Software available at http://torcs. sourceforge. net, 4(6):2, 2000. 3

    2000

  44. [52]

    Bits: Bi-level imitation for traffic simulation

    Danfei Xu, Yuxiao Chen, Boris Ivanovic, and Marco Pavone. Bits: Bi-level imitation for traffic simulation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 2929–2936. IEEE, 2023. 3

    2023

  45. [53]

    V2v4real: A real-world large- scale dataset for vehicle-to-vehicle cooperative perception

    Runsheng Xu, Xin Xia, Jinlong Li, Hanzhao Li, Shuo Zhang, Zhengzhong Tu, Zonglin Meng, Hao Xiang, Xi- aoyu Dong, Rui Song, et al. V2v4real: A real-world large- scale dataset for vehicle-to-vehicle cooperative perception. In Proceedings of the IEEE/CVF Conference on Computer Vi...

    2023

  46. [54]

    Street gaussians for modeling dynamic ur- ban scenes

    Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians for modeling dynamic ur- ban scenes. arXiv preprint arXiv:2401.01339, 2024. 2, 3

    2024 arXiv

  47. [55]

    Ner- fvs: Neural radiance fields for free view synthesis via geom- etry scaffolds

    Chen Yang, Peihao Li, Zanwei Zhou, Shanxin Yuan, Bing- bing Liu, Xiaokang Yang, Weichao Qiu, and Wei Shen. Ner- fvs: Neural radiance fields for free view synthesis via geom- etry scaffolds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p...

    2023

  48. [56]

    Emernerf: Emergent spatial-temporal scene decomposition via self-supervision

    Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Se- ung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, et al. Emernerf: Emergent spatial-temporal scene decomposition via self-supervision. arXiv preprint arXiv:2311.02077, 2023. 3

    2023 arXiv

  49. [57]

    Depth any- thing v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiao- gang Xu, Jiashi Feng, and Hengshuang Zhao. Depth any- thing v2. arXiv preprint arXiv:2406.09414, 2024. 1

    2024 arXiv

  50. [58]

    Unisim: A neural closed-loop sensor simulator

    Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Mani- vasagam, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Ur- tasun. Unisim: A neural closed-loop sensor simulator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1389–1399, 2023. 2, 3

    2023

  51. [59]

    Mip-splatting: Alias-free 3d gaussian splatting

    Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19447– 19456, 2024. 3

    2024

  52. [60]

    Sgd: Street view synthesis with gaussian splatting and diffusion prior

    Zhongrui Yu, Haoran Wang, Jinze Yang, Hanzhang Wang, Zeke Xie, Yunfeng Cai, Jiale Cao, Zhong Ji, and Mingming Sun. Sgd: Street view synthesis with gaussian splatting and diffusion prior. arXiv preprint arXiv:2403.20079, 2024. 3

    2024 arXiv

  53. [61]

    Gaussian in the wild: 3d gaussian splatting for unconstrained image collections.arXiv preprint arXiv:2403.15704, 2024

    Dongbin Zhang, Chuming Wang, Weitao Wang, Peihao Li, Minghan Qin, and Haoqian Wang. Gaussian in the wild: 3d gaussian splatting for unconstrained image collections.arXiv preprint arXiv:2403.15704, 2024. 8

    2024 arXiv

  54. [62]

    Ray priors through reprojection: Improv- ing neural radiance fields for novel view extrapolation

    Jian Zhang, Yuanqing Zhang, Huan Fu, Xiaowei Zhou, Bowen Cai, Jinchi Huang, Rongfei Jia, Binqiang Zhao, and Xing Tang. Ray priors through reprojection: Improv- ing neural radiance fields for novel view extrapolation. In Proceedings of the IEEE/CVF Conference on Computer Vision...

    2022

  55. [63]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586–595, 2018. 6

    2018

  56. [64]

    Drivedreamer4d: World models are effective data machines for 4d driving scene rep- resentation

    Guosheng Zhao, Chaojun Ni, Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, Boyuan Wang, Youyi Zhang, Wenjun Mei, and Xingang Wang. Drivedreamer4d: World models are effective data machines for 4d driving scene rep- resentation. arXiv preprint arXiv:2410.13571, 2024. 3

    arXiv

  57. [65]

    Guided conditional diffusion for controllable traffic simula- tion

    Ziyuan Zhong, Davis Rempe, Danfei Xu, Yuxiao Chen, Sushant Veer, Tong Che, Baishakhi Ray, and Marco Pavone. Guided conditional diffusion for controllable traffic simula- tion. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 3560–3566. IEEE, 2023. 3

    2023

  58. [66]

    Hugs: Holistic urban 3d scene understanding via gaus- sian splatting

    Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugs: Holistic urban 3d scene understanding via gaus- sian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages ...

    2024

  59. [67]

    Feature 3dgs: Supercharg- ing 3d gaussian splatting to enable distilled feature fields

    Shijie Zhou, Haoran Chang, Sicheng Jiang, Zhiwen Fan, Ze- hao Zhu, Dejia Xu, Pradyumna Chari, Suya You, Zhangyang Wang, and Achuta Kadambi. Feature 3dgs: Supercharg- ing 3d gaussian splatting to enable distilled feature fields. In Proceedings of the IEEE/CVF Conference on Comp...

    2024

  60. [68]

    Drivinggaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes

    Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages ...

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.