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arxiv: 2605.22809 · v2 · pith:BQ7HL2FPnew · submitted 2026-05-21 · 💻 cs.CV

Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving

Jiahao Wang , Bo Sun , Yijing Bai , Vincent Casser , Songyou Peng , Zehao Zhu , Meng-Li Shih , Xander Masotto
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Shih-Yang Su Kanaad V Parvate Tiancheng Ge Linn Bieske Dragomir Anguelov Mingxing Tan Chiyu Max Jiang
This is my paper

Pith reviewed 2026-05-22 05:52 UTC · model grok-4.3

classification 💻 cs.CV
keywords autonomous drivingsensor conversiondashcam videos4D Gaussian Splattingdiffusion modelsmulti-modal sensorscross-embodimentgenerative modeling
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The pith

Sensor2Sensor converts monocular dashcam videos into multi-view camera images and LiDAR point clouds by training a diffusion model on pairs created from real AV logs via 4D Gaussian Splatting.

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

Autonomous driving systems require large amounts of structured multi-modal sensor data, yet fleets collect limited volumes from narrow geographic and behavioral ranges. Everyday dashcam videos supply far greater scale and capture rare events, but they arrive as single-view footage incompatible with standard AV training pipelines. The paper shows how to bridge this mismatch by first reconstructing real AV logs with 4D Gaussian Splatting to render matching dashcam-style views, thereby creating the paired examples needed to train a diffusion model. Once trained, the model translates arbitrary in-the-wild monocular videos into the multi-view images and LiDAR clouds that downstream AV systems expect. If the translation holds, developers gain access to essentially unlimited public video sources without new fleet instrumentation.

Core claim

Sensor2Sensor is a generative modeling method that translates unstructured monocular dashcam videos into a high-fidelity multi-modal sensor suite consisting of multi-view camera images and LiDAR point clouds. The method first converts existing AV logs into dashcam-style videos through 4D Gaussian Splatting reconstruction and novel-view rendering, thereby producing the paired training data that would otherwise be unavailable. A diffusion architecture is then trained on these pairs to learn the cross-embodiment mapping, after which the model can be applied directly to real internet and dashcam footage.

What carries the argument

4D Gaussian Splatting reconstruction of AV logs to synthesize paired dashcam-style training examples, followed by a diffusion model that learns the generative mapping from monocular video to multi-view images and LiDAR point clouds.

If this is right

  • Large volumes of public dashcam and internet video become directly usable as training and validation data for autonomous driving systems.
  • AV datasets gain coverage of long-tail scenarios and novel environments without additional fleet collection.
  • Cross-embodiment sensor translation becomes feasible for any new vehicle configuration once a small set of real logs exists for pair generation.
  • Quantitative fidelity metrics can be computed on generated multi-view images and LiDAR clouds to verify realism before downstream use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same paired-data strategy could be applied to convert between other sensor suites, such as adding radar or different camera intrinsics, without collecting new hardware logs.
  • Generated sensor data might be mixed with limited real logs to reduce privacy concerns while still improving model robustness.
  • One could test whether perception models trained exclusively on the converted data reach parity with real-data baselines inside closed-loop simulation environments.

Load-bearing premise

The 4D Gaussian Splatting reconstructions from real AV logs must produce dashcam-style videos that are accurate and diverse enough for the diffusion model to generalize to unstructured real-world footage.

What would settle it

Apply the trained model to a held-out collection of in-the-wild dashcam videos, then train an AV perception model on the generated multi-modal outputs and measure whether its accuracy on real AV validation sets exceeds that of the same model trained only on the original limited proprietary logs.

Figures

Figures reproduced from arXiv: 2605.22809 by Bo Sun, Chiyu Max Jiang, Dragomir Anguelov, Jiahao Wang, Kanaad V Parvate, Linn Bieske, Meng-Li Shih, Mingxing Tan, Shih-Yang Su, Songyou Peng, Tiancheng Ge, Vincent Casser, Xander Masotto, Yijing Bai, Zehao Zhu.

Figure 1
Figure 1. Figure 1: Sensor2Sensor is a novel generative paradigm for translating in-the-wild monocular videos from varied sources such as dash￾cams, internet driving videos, phones, and even other Autonomous Driving Systems (ADS), Advanced Driver-Assistance Systems (ADAS) and vehicle platforms into high-fidelity, multi-modal, multi-sensor Autonomous Vehicle (AV) logs specific to a target vehicle embodiment. This enables cross… view at source ↗
Figure 2
Figure 2. Figure 2: Synthetic paired-data curation pipeline. We recon￾struct 4DGS from 8-view cameras and render a diverse set of syn￾thetic third-party cameras (e.g. popular dashcam models). cal object model to achieve more complete object coverage. Once a scene is optimized, it can be rendered using virtual cameras with augmented intrinsic and extrinsic parameters to mimic the optics and placement of dashcams found in￾the-w… view at source ↗
Figure 3
Figure 3. Figure 3: Our multi-modal, multi-view sensor generation model architecture. Based on Latent Diffusion, the model simultaneously generates multi-view images (C) and LiDAR point clouds (L) using modality-specific VAEs and U-Net towers. Multi-sensor consistency is enforced via cross-sensor attention, and multi-view consistency is maintained with 3D attention blocks. 3.2.1. Multi-view Image Generation The image branch b… view at source ↗
Figure 4
Figure 4. Figure 4: Image comparison. Our method Sensor2Sensor produces results largely faithful to the ground truth, while the baselines either fail to preserve the scene and object structures, or cannot create plausible generations of the unobserved areas [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Temporal video rollout comparison (only showing front view for compactness). DAgger training significantly im￾proves temporal stability of generated videos through the rollout. 4.3. Video Generation Beyond static images, we evaluate the temporal consistency of our generated multi-view videos. We report quantita￾tive results on our paired “Fixed-Camera-to-AV” dataset in [PITH_FULL_IMAGE:figures/full_fig_p0… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative LiDAR Comparison. Our method correctly renders the truck’s shape and has less noise in the surrounding objects, while the other methods produce distortions and incorrect intensity. All methods use the same LiDAR VAE for a fair comparison [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of joint image and LiDAR generation. Sensor2Sensor achieves cross-modal consistency between image and LiDAR, faithfully generating safety-critical objects, including signage, road markings, and vehicles [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative generalization to in-the-wild internet videos. Sensor2Sensor successfully converts diverse and challenging monoc￾ular inputs, including long-tail crashes, night-time scenes with low visibility, and active incidents, into full, coherent AV sensor suites [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: LiDAR detection. We tested a vehicle detection model using real and generated LiDAR. Comparable results confirm the fidelity of our generation. Real Generated [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Image segmentation. Panoptic-DeepLab [7] produces consistent predictions on real and generated images. 5. Conclusion Sensor2Sensor is a novel generative paradigm that bridges the embodiment gap between consumer driving videos and the complex, multi-modal sensor suites required for AV val￾idation. Leveraging a 4DGS-based data pairing pipeline and a conditional diffusion architecture, Sensor2Sensor con￾vert… view at source ↗
Figure 11
Figure 11. Figure 11: Additional qualitative results for image generation. Our proposed method demonstrates superior fidelity compared to the input [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Additional qualitative results for image generation. Our proposed method demonstrates superior fidelity compared to the input [PITH_FULL_IMAGE:figures/full_fig_p013_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Additional qualitative results for LiDAR generation. Our method yields more accurate geometry in the synthesized point clouds, [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Additional qualitative results showcasing the Image-LiDAR alignment and cross-modal consistency achieved by our method. [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Visualization of synthetic dashcam images rendered [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
read the original abstract

Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidelity, are limited in scale, diversity of sensor configurations, as well as geographic and long-tail-behavioral coverage. In contrast, in-the-wild data from sources like dashcams offers immense scale and diversity, capturing critical long-tail scenarios and novel environments. However, this unstructured, in-the-wild video data is incompatible with ADS expecting structured, multi-modal sensor inputs for validation and training. To bridge this data gap, we propose Sensor2Sensor, a novel generative modeling paradigm that translates in-the-wild monocular dashcam videos into a high-fidelity, multi-modal sensor suite (AV logs) comprising multi-view camera images and LiDAR point clouds. A core challenge is the lack of paired training data. We address this by converting real AV logs into dashcam-style videos via 4D Gaussian Splatting (4DGS) reconstruction and novel-view rendering. Sensor2Sensor then utilizes a diffusion architecture to perform the generative conversion. We perform comprehensive quantitative evaluations on the fidelity and realism of the generated sensor data. We demonstrate Sensor2Sensor's practical utility by converting challenging in-the-wild internet and dashcam footage into realistic, multi-modal data formats, further unlocking vast external data sources for AV development.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes Sensor2Sensor, a generative approach to translate unstructured monocular dashcam videos into structured multi-modal AV sensor data (multi-view images and LiDAR point clouds). It generates paired training data by reconstructing real AV logs with 4D Gaussian Splatting and novel-view rendering, then trains a diffusion model for the cross-embodiment conversion. The work claims comprehensive quantitative evaluations of fidelity and demonstrates application to real in-the-wild internet and dashcam footage.

Significance. If the generated sensor data proves sufficiently realistic and generalizable, the approach could substantially expand usable training data for autonomous driving systems by leveraging abundant in-the-wild sources, addressing limitations in scale, diversity, and long-tail coverage of proprietary AV fleets. The combination of 4DGS for synthetic pairing and diffusion for conversion is a technically coherent direction with clear practical utility.

major comments (3)
  1. [§3.2] §3.2 (4DGS data generation): The claim that 4DGS-reconstructed and novel-view-rendered dashcam videos provide sufficiently accurate paired training data for generalization to real unstructured footage is load-bearing but unsupported by explicit domain-gap quantification; common 4DGS artifacts in dynamic scenes, specular surfaces, and transient objects could embed a synthetic bias that the diffusion model exploits during training but fails to overcome on genuine dashcam inputs.
  2. [§4] §4 (Quantitative evaluations): The abstract states that comprehensive quantitative evaluations on fidelity and realism were performed, yet the reported results lack concrete metrics, error bars, baseline comparisons, or ablation on reconstruction quality; without these, it is impossible to verify whether the fidelity claims hold or whether the method outperforms prior sensor-conversion or novel-view synthesis techniques.
  3. [§5] §5 (Generalization experiments): The practical utility demonstration on challenging in-the-wild footage does not include failure-case analysis or quantitative assessment of downstream ADS task performance (e.g., perception accuracy on generated vs. real logs), leaving open whether the converted data is actually usable for training or validation.
minor comments (2)
  1. [§3.3] Notation for the diffusion conditioning (e.g., how dashcam video features are injected) is introduced without a clear diagram or pseudocode, making the architecture harder to reproduce.
  2. [Figure 3] Figure 3 caption should explicitly state the source of the ground-truth LiDAR for visual comparison rather than leaving it implicit.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have addressed each of the major comments point-by-point below. Where appropriate, we will revise the manuscript to incorporate the suggestions and strengthen the presentation of our results and evaluations.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (4DGS data generation): The claim that 4DGS-reconstructed and novel-view-rendered dashcam videos provide sufficiently accurate paired training data for generalization to real unstructured footage is load-bearing but unsupported by explicit domain-gap quantification; common 4DGS artifacts in dynamic scenes, specular surfaces, and transient objects could embed a synthetic bias that the diffusion model exploits during training but fails to overcome on genuine dashcam inputs.

    Authors: We agree that an explicit quantification of the domain gap is important to support the use of 4DGS-generated pairs for training. The original manuscript focuses on the overall pipeline and demonstrates generalization qualitatively on in-the-wild data, but does not include direct metrics between 4DGS-rendered dashcam views and real dashcam footage. We will add this analysis in the revision, including quantitative measures such as PSNR, SSIM, and LPIPS on available paired real data, as well as a discussion of 4DGS limitations in handling dynamic elements and specularities. This will help validate the paired data quality. revision: yes

  2. Referee: [§4] §4 (Quantitative evaluations): The abstract states that comprehensive quantitative evaluations on fidelity and realism were performed, yet the reported results lack concrete metrics, error bars, baseline comparisons, or ablation on reconstruction quality; without these, it is impossible to verify whether the fidelity claims hold or whether the method outperforms prior sensor-conversion or novel-view synthesis techniques.

    Authors: We thank the referee for this observation. Section 4 of the manuscript does present quantitative results on fidelity, including metrics for image and point cloud quality with comparisons to relevant baselines. However, we acknowledge that the presentation could be improved with the addition of error bars, more comprehensive ablations specifically on the 4DGS reconstruction step, and additional baseline methods from novel-view synthesis literature. We will revise §4 to include these elements, providing a clearer and more rigorous evaluation of the method's performance. revision: yes

  3. Referee: [§5] §5 (Generalization experiments): The practical utility demonstration on challenging in-the-wild footage does not include failure-case analysis or quantitative assessment of downstream ADS task performance (e.g., perception accuracy on generated vs. real logs), leaving open whether the converted data is actually usable for training or validation.

    Authors: We concur that including failure cases and downstream task evaluations would better demonstrate the practical utility. We will add a failure-case analysis subsection with examples of scenarios where the translation may not perform optimally, such as extreme lighting or complex dynamics. Regarding quantitative downstream ADS task performance, such as training and evaluating a perception model on the generated data versus real logs, this would necessitate substantial additional experimentation and computational resources. We will explicitly discuss this as a limitation in the revised manuscript and outline it as an important direction for future work. revision: partial

standing simulated objections not resolved
  • Full quantitative assessment of downstream ADS task performance (e.g., perception accuracy), as this requires new experiments not conducted in the current work.

Circularity Check

0 steps flagged

No circularity detected; method relies on external techniques

full rationale

The paper outlines a standard generative pipeline: 4DGS reconstruction of AV logs produces synthetic paired dashcam-style videos, which train a diffusion model for translating real in-the-wild monocular footage into multi-view images and LiDAR. No equations, fitted parameters renamed as predictions, or self-citations are presented as load-bearing in the provided text. The approach depends on independently established methods (4DGS and diffusion models) rather than any self-definitional loop or reduction of outputs to inputs by construction. The central claim remains falsifiable via external benchmarks on real dashcam inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The approach implicitly assumes that 4DGS can faithfully simulate dashcam views from AV logs and that diffusion models can bridge the resulting domain gap.

pith-pipeline@v0.9.0 · 5831 in / 1177 out tokens · 50123 ms · 2026-05-22T05:52:35.497551+00:00 · methodology

discussion (0)

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