Cam2Sim: Neural Scenario Reconstruction for Closed-Loop Autonomous Driving Simulation

Andrea Stocco; Constantin Carste; Davide Jannussi; Stefano Carlo Lambertenghi

Cam2Sim: Neural Scenario Reconstruction for Closed-Loop Autonomous Driving Simulation

T0 review · 2 major / 0 minor · reviewed 2026-07-11 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Cam2Sim turns real driving camera recordings into closed-loop CARLA scenarios whose Gaussian-Splatting views make an end-to-end driver behave like the real runs.

desk verdict Useful end-to-end tool paper with a real closed-loop win on one urban log; the GS-overfitting stress test is fair but does not erase the contribution. read the letter →

arxiv 2607.04770 v1 pith:FG73K7LT submitted 2026-07-06 cs.SE

Davide Jannussi , Stefano Carlo Lambertenghi , Constantin Carste , Andrea Stocco This is my paper

classification cs.SE

keywords autonomousdrivingsim-to-realgapGaussianSplattingscenarioreconstructionCARLAclosed-looptestingneuralrenderingend-to-end

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

Simulation is the only practical way to stress-test autonomous drivers repeatedly, yet camera-based models often fail in the simulator because synthetic images look nothing like the road. Cam2Sim takes ordinary front-camera frames and poses from a real drive, rebuilds the road map, path, and parked cars inside CARLA, then trains local Gaussian Splatting models so the simulator can re-render those same viewpoints in a photorealistic style. In a closed-loop test on a 450 m residential street, a DAVE-2 steering network that always left the road under raw CARLA images completed every run when it saw the Gaussian-Splatting images and produced trajectories close to the real-world reference. The tool therefore claims that geometry reconstruction plus neural rendering is enough to shrink the sim-to-real gap for camera-driven systems and make simulation results trustworthy for testing.

What carries the argument

Cam2Sim pipeline: camera/pose extraction, OpenStreetMap road reconstruction, parked-vehicle placement, local Gaussian Splatting training, and closed-loop CARLA execution that feeds the GS-rendered camera stream to the system under test.

What would settle it

Repeat the identical closed-loop DAVE-2 protocol on a second, independently collected urban route whose visual appearance or geometry differs from the original street; if the GS-augmented scenario no longer completes while the real vehicle does, or if raw CARLA suddenly matches real behavior, the central claim fails.

Watch

Extended reading notes

Core claim

On a real urban recording, Cam2Sim-reconstructed CARLA scenarios rendered with Gaussian Splatting reduce the visual discrepancy relative to standard simulator images and produce closed-loop DAVE-2 behavior that matches real-world reference runs (3/3 completions), while the identical reconstructed scenario under raw CARLA rendering fails every run (3/3).

Load-bearing premise

That rebuilding one static urban street and training Gaussian Splatting on that same route, then testing one fixed-speed camera-only driver three times, is enough to show the method improves closed-loop testing realism in general.

Editorial extensions

If this is right

Camera-based ADS that currently fail under synthetic rendering can be re-tested inside reconstructed real routes without new physical drives.
Trajectory-replay and closed-loop logs become comparable across real, sim-only, and GS-rendered domains for the same geometry.
Parked-vehicle and map assets extracted from ordinary camera logs become reusable CARLA scenarios for regression testing.
Local Gaussian Splatting models can be swapped or retrained when a route's appearance changes without rebuilding the entire map.

Reading between the lines

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

If the same pipeline works for multi-camera or LiDAR-only stacks, the bottleneck for trustworthy sim testing may shift from geometry fidelity to dynamic-object modeling.
The 3/3 versus 0/3 completion gap suggests visual domain gap, not control or map error, was the dominant failure mode for this particular end-to-end model.
Extending the method to moving traffic and weather would turn one-off route reconstructions into reusable digital twins for continuous integration of perception models.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The thing worth knowing is that Cam2Sim is a working pipeline from real camera+pose logs to playable CARLA scenarios with Gaussian Splatting camera feeds, and on their Munich residential run the GS path lets DAVE-2 finish 3/3 while stock CARLA fails 3/3. That is a concrete, reproducible result with a public artifact, not just another rendering demo.

What is new is the assembly: ROS extraction, OSM/OpenDRIVE map, parked-car detection (FCOS3D or PointPillars plus manual cleanup), COLMAP+Nerfstudio GS, trajectory replay, and closed-loop ADS control in one tool. Prior work either renders novel views without an executable sim, or rebuilds scenarios without closing the camera gap. They measure geometric fidelity (mIoU 0.774 overall, road 0.847, cars 0.577 over 3144 frames) against real semantic maps, and they report Fréchet, corridor, and steering-jitter numbers for the successful GS runs against real reference trajectories. The real-world and sim-only baselines are independent of the GS model, so the completion contrast is not circular.

Soft spots are real and mostly already flagged by the authors as preliminary. Image-quality metrics are still TODOs in the manuscript, so the claim that GS reduces the visual gap is only half-measured. Evaluation is one 450 m street, one fixed-speed camera-only DAVE-2, n=3 per domain. The stress-test concern is fair: GS is trained on the same route’s frames and later renders from nearby closed-loop poses, so some of the win could be route-specific appearance memorization rather than a general sim-to-real fix. That does not make the result fake; it means the paper currently shows “this pipeline can make closed-loop testing work on a logged drive where stock CARLA does not,” not “GS always generalizes.” Dynamic objects and broader ADS/models are future work, which is appropriate for a tool paper.

Citations look normal for this niche; math is light because this is systems work. Who it is for: people building ADS regression suites from logs and anyone who needs CARLA scenarios that look more like the real camera stream. I would send it to peer review. Complete the image metrics, add at least one more scenario or a modest pose/lighting deviation check, and it is a solid ASE tool paper. Engage with the artifact if you care about closed-loop camera testing.

Referee Report

2 major / 0 minor

Summary. Cam2Sim is a tool paper that reconstructs playable CARLA scenarios from real-world front-camera images and poses, then augments them with local Gaussian Splatting so a camera-based ADS can be tested closed-loop on observations that resemble the original recording. The pipeline covers ROS extraction, parked-vehicle detection (FCOS3D or PointPillars plus manual refinement), OpenStreetMap/OpenDRIVE map generation, trajectory and asset export, COLMAP+Nerfstudio GS training with sky masks and coordinate alignment, and both trajectory-replay and closed-loop execution with a DAVE-2 model. On one 450 m Munich residential street the authors report geometric reconstruction mIoU of 0.774 over 3144 frames (road 0.847, cars 0.577), and closed-loop results in which GS-augmented simulation completes 3/3 runs with trajectories closer to real-world references while the simulation-only CARLA baseline fails 3/3 (Table 1). Image-generation quality metrics are left as unfinished TODOs.

Significance. If the central claim holds, Cam2Sim would be a practically useful bridge between real recordings and closed-loop CARLA testing for camera-based ADS, addressing a well-known sim-to-real limitation that pure scenario reconstruction or pure neural rendering alone do not solve. Strengths that should be credited include a complete, publicly released artifact (Apache-2.0 GitHub with ROS bag, ADS, scripts, and dependency documentation), a clear five-stage pipeline, quantified geometric fidelity on thousands of frames, and a stark closed-loop contrast (GS 3/3 complete vs sim-only 3/3 fail) against independent real-world reference runs. For a tool paper these engineering and reproducibility contributions are material even if the empirical scope remains preliminary.

major comments (2)

Section 3.2 (Image Generation Quality) is unfinished: three full-reference metrics and their results table are still TODO placeholders, and the abstract/intro claim that GS 'reduces the visual gap' is therefore unsupported by reported numbers. Completing this subsection with the promised metrics (and preferably a modest off-trajectory or lighting-variation check) is load-bearing for attributing the closed-loop gains to improved visual fidelity rather than route-specific appearance memorization.
Section 3 / Collection Scenario and Behavior Fidelity (Table 1): the closed-loop claim rests on a single 450 m residential route, one fixed-speed (20 km/h PID) camera-only DAVE-2 model, and n=3 runs per domain. Because GS is trained on the same route's frames/poses (Step 4) that later supply closed-loop viewpoints, the 3/3 vs 0/3 completion gap may partly reflect static-scene overfitting rather than generalizable sim-to-real reduction. At minimum the manuscript should acknowledge this scope limit and, if possible, add a small held-out viewpoint or second-route check; without that, the generality claim for 'closed-loop ADS testing' should be narrowed to the demonstrated setting.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: Cam2Sim’s behavioral claims are empirical comparisons against independent real-world and simulation-only baselines, not quantities forced by GS training or self-citation.

full rationale

Cam2Sim is an empirical tool paper, not a first-principles derivation. The load-bearing claim—that GS-rendered observations improve closed-loop DAVE-2 fidelity relative to stock CARLA—is evaluated by completion rate, Fréchet distance, corridor violations, lateral excess, and steering jitter against real-world reference runs and a simulation-only baseline that never sees the GS model (Section 3.3, Table 1). Those external references and failure modes are not defined in terms of the GS fit, nor recovered by renaming a fitted parameter. Geometric mIoU (Section 3.1) likewise compares reconstructed semantic maps to real-world semantic observations from the same viewpoints. Self-citations (e.g., prior sim-to-real and ADS-testing work by overlapping authors) appear only as motivation and related work; none supply a uniqueness theorem or ansatz that forces the completion-rate result. Route-specific GS overfitting is a generalization/correctness concern, not circularity by construction. Image-quality metrics are unfinished (TO DO placeholders) but that incompleteness does not create a circular step. Score 0; steps empty.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central behavioral claim rests on standard ADS-testing assumptions (static parked traffic, OSM geometry, CARLA as execution substrate, DAVE-2 as representative camera E2E model) plus engineering choices in detection, map export, and local GS training. No new physical entities are postulated; free parameters are ordinary pipeline knobs rather than fitted universal constants.

free parameters (4)

GS training configuration (splatfacto / splatfacto-big, chunking, sky masks)
Local Nerfstudio training choices and frame-interval selection control visual fidelity and are not derived from first principles; they are set for the target route.
Closed-loop target speed (20 km/h PID)
Longitudinal control is fixed by hand for all domains; behavioral comparison depends on this operating point.
Parked-vehicle detection and clustering thresholds / manual refinement
FCOS3D or PointPillars detections plus confidence-weighted averaging and optional GUI edits determine spawn poses that affect the reconstructed scene.
Trajectory export convention (center vs rear-axle) and coordinate alignment
Hand-chosen mapping between recorded poses and CARLA vehicle transforms affects replay and closed-loop start state.

assumptions (5)

domain assumption OpenStreetMap geometry retrieved for the trajectory or address is accurate enough to serve as the CARLA OpenDRIVE map for the target street.
Map generation step (Section 2.2–2.3) treats OSM/Overpass output as ground truth for road layout.
domain assumption The scene can be treated as static aside from the ego vehicle (parked cars only; no dynamic traffic or pedestrians that must be reconstructed).
Pipeline reconstructs parked vehicles and static GS content; future-work section acknowledges missing dynamic-object reconstruction.
domain assumption A single camera-based DAVE-2 model with independent PID speed control is a sufficient system under test to assess closed-loop fidelity of the reconstructed observations.
All behavior results (Section 3.3, Table 1) use this one architecture and fixed speed.
domain assumption Semantic IoU of road/car/background under trajectory replay is a meaningful proxy for geometric reconstruction quality usable for ADS testing.
Section 3.1 reports mIoU as the primary reconstruction metric.
standard math Standard computer-vision and SfM tools (COLMAP, SegFormer sky masks, Nerfstudio) produce usable GS models when poses and frames are supplied.
GS preparation (Section 2.4) relies on these established pipelines without re-deriving them.

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Cite this review

Pith. "Pith review of Cam2Sim: Neural Scenario Reconstruction for Closed-Loop Autonomous Driving Simulation." pith.science (2026). https://pith.science/paper/FG73K7LT

@misc{pith2026260704770,
  author       = {Pith},
  title        = {Pith review of: Cam2Sim: Neural Scenario Reconstruction for Closed-Loop Autonomous Driving Simulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FG73K7LT}},
  note         = {Machine review of arXiv:2607.04770}
}

read the original abstract

Simulation-based testing enables safe and repeatable evaluation of autonomous driving systems, but its effectiveness is limited by the gap between synthetic simulator outputs and real-world camera observations. To address this problem, we present Cam2Sim, a tool that transforms real-world driving recordings into playable CARLA simulation scenarios. Starting from camera images and poses, Cam2Sim reconstructs road geometry, ego trajectories, parked vehicles, and simulation assets, and augments the reconstructed environment with Gaussian Splatting to render camera observations that resemble the original recording. The framework supports ROS-based data extraction, parked-vehicle detection, OpenStreetMap-based map generation, CARLA scenario construction, Gaussian Splatting training, trajectory replay, and closed-loop execution with a system under test. We validate Cam2Sim on a real-world urban-driving scenario with a camera-based end-to-end driving model, comparing reconstruction quality, image-generation quality, and closed-loop behavior against both a simulation-only baseline and the real-world target. Results show that Gaussian-Splatting-based rendering reduces the visual gap with respect to standard simulator rendering and improves behavioral similarity to the real-world reference runs. The artifact is publicly available at https: //github.com/ast-fortiss-tum/cam2sim, and a screencast showing the tool is available at https://youtu.be/KmZ74l1__lI

Figures

Figures reproduced from arXiv: 2607.04770 by the authors.

**Figure 2.** Validation setup and results: (A) the ADS, (B) the autonomous-driving platform used to collect the target recording, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Reference graph

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Pith tools

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

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Luciano Baresi, Davide Yi Xian Hu, Andrea Stocco, and Paolo Tonella. 2025. Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models. InProceedings of 47th International Conference on Software Engineering (ICSE ’25). IEEE

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Matteo Biagiola, Andrea Stocco, Vincenzo Riccio, and Paolo Tonella. 2024. Two is better than one: digital siblings to improve autonomous driving testing.Empirical Cam2Sim : Neural Scenario Reconstruction for Closed-Loop Autonomous Driving Simulation ASE ’26, October 12–16, 2026, Munich, Germany Softw. Engg.29, 4 (May 2024), 33 pages. doi:10.1007/s10664-02...

doi:10.1007/s10664-024-10458-4 2024

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Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba

Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba. 2016. End to End Learning for Self-Driving Cars. arXiv:1604.07316 [cs.CV]

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Fortuna: Presenting the 5G-connected automated vehicle prototype of the project PROVIDENTIA.Researchgate. https://doi. org/10.13140/RG2, 24402.91842 (2019). doi:10.13140/rg.2.2.24402.91842

work page doi:10.13140/rg2 2019

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Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele

[7] [7]

In2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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doi:10.1109/cvpr.2016.350 2016

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Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017. CARLA: An Open Urban Driving Simulator. InProceedings of the 1st Annual Conference on Robot Learning (Proceedings of Machine Learning Research, Vol. 78), Sergey Levine, Vincent Vanhoucke, and Ken Goldberg (Eds.). PMLR, 1–16. https://proceedings.mlr.press/v78/dosovit...

2017