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REVIEW 5 major objections 6 minor 86 references

Pose Optimization for Autonomous Driving Datasets using Neural Rendering Models

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read NeRF-based pose refinement measurably improves sensor poses in four public driving datasets.

desk verdict A useful and honest application of the authors' own NeRF-based calibration methods to refine poses on four driving datasets, but the claim of 'significant improvements in sensor pose accuracy' overreaches what internal-consistency metrics can show. read the letter →

arxiv 2504.15776 v1 pith:OHQMCSC5 submitted 2025-04-22 cs.CV cs.RO

Quentin Herau , Nathan Piasco , Moussab Bennehar , Luis Roldão , Dzmitry Tsishkou , Bingbing Liu , Cyrille Migniot , Pascal Vasseur
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Cédric Demonceaux
This is my paper · ORCID
classification cs.CVcs.RO
keywords NeuralRadianceFieldssensorposerefinementextrinsiccalibrationtrajectoryoptimizationautonomousdrivingdatasetsnovelviewsynthesismulti-sensorfusionevaluationwithoutgroundtruth
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

Public driving datasets carry small but systematic errors in sensor poses and camera-to-LiDAR calibration, and those errors can dominate the gains reported by downstream models. This paper claims that a neural-radiance-field optimization pipeline can refine both the vehicle trajectory and the extrinsic calibration, making the dataset poses more accurate even when no ground-truth poses are available. It applies the pipeline to KITTI-360, NuScenes, PandaSet, and Waymo, and reports consistent improvements across three families of proxy metrics: keypoint reprojection and track length, novel-view-synthesis quality, and mesh-to-LiDAR geometric alignment. The central contribution, read sympathetically, is a practical cleaning of public benchmarks together with an evaluation protocol for judging pose quality without ground truth.

What carries the argument

The load-bearing mechanism is a differentiable neural scene representation trained with photometric and LiDAR-geometric losses, through which pose and calibration corrections are back-propagated. The optimized pose of sensor $i$ at time $t$ is written $wT_i(t) = E_{\mathrm{cor}}(i)\,\, {}^iT_r\, T_{\mathrm{cor}}(t)\, T_r(t)$, where $T_r$ is the interpolated reference trajectory, $E_{\mathrm{cor}}(i)$ is a shared per-sensor calibration correction, and $T_{\mathrm{cor}}(t)$ is a continuous trajectory correction learned by a small network over time. The pipeline first removes dynamic objects by masking annotated bounding boxes, splits long sequences into subsequences small enough for a NeRF, optimizes the shared extrinsic correction across many subsequences in parallel, then freezes calibration and optimizes each trajectory. MOISST is the multi-sensor model that fuses cameras and LiDAR under rigid sensor constraints; SOAC is the overlap-aware alternative with separate scene representations per camera; two open-source rendering models, one NeRF-based and one Gaussian-splatting-based, serve as independent baselines and evaluation renderers.

What would settle it

Run the pipeline on a synthetic or motion-capture urban dataset with known ground-truth poses, starting from deliberately perturbed poses; if the optimized poses do not move closer to the ground truth on absolute trajectory error, or if the proxy metrics improve while true pose error grows, the central claim is refuted.

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Extended reading notes

Core claim

The paper's central claim is that jointly optimizing sensor poses and extrinsic calibration inside a neural radiance field yields measurably better dataset poses than the ones shipped with four major autonomous driving datasets. Using MOISST as the main model and SOAC as a robustness-focused alternative, with trajectory correction added to both, the authors report that optimized poses reduce reprojection error, lengthen keypoint tracks, improve PSNR, SSIM, and LPIPS across three separate rendering models, and tighten the alignment between image-derived meshes and accumulated LiDAR. The improvements are consistent for MOISST on all four datasets; SOAC improves NuScenes and PandaSet but regresses on KITTI-360 and Waymo. The paper also shows that KITTI-360's original poses are already precise, with relative gains around one to two percent, while NuScenes benefits the most because its published poses are planar and lack a reliable Z-axis component.

Load-bearing premise

The evaluation assumes that the proxy metrics—reprojection error, rendering quality, and mesh-to-LiDAR distance—actually track true pose accuracy, even though the rendering metrics are partly circular because the poses are optimized by training the same kind of model that then scores them.

Editorial extensions

If this is right

  • If the optimized poses are accepted, the four datasets become higher-fidelity benchmarks: downstream models are trained and evaluated against geometry closer to the true scene, reducing the risk that reported gains come from overfitting to pose noise.
  • Neuro-renderers trained from scratch on the optimized poses render with higher PSNR and SSIM and lower LPIPS, so any task built on novel view synthesis inherits the improvement.
  • Mesh-to-LiDAR precision rises under MOISST on all four datasets, indicating better camera-to-LiDAR alignment and therefore better multi-sensor fusion for mapping and localization.
  • For NuScenes, where the published poses carry no Z information, trajectory optimization reconstructs a usable vertical component and produces the largest metric gains, with reprojection error dropping from 1.339 to 0.852 pixels.

Reading between the lines

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

  • My inference: if the proxy metrics genuinely track pose error, the released poses could serve as pseudo-ground truth for calibrating and evaluating localization systems on these datasets, but that status would need independent confirmation on a set with known poses first.
  • My inference: the method will inherit NeRF's failure modes, such as dark tunnels, repetitive walls, and distant structures, so a natural extension is to swap in a more failure-tolerant renderer, for instance Gaussian splatting with depth priors, and test whether the same pipeline still holds.
  • My inference: because the rendering metrics are produced by the same family of models used for optimization, the reprojection and geometric metrics carry most of the evidential weight; a useful next step would be a sign-agreement analysis that excludes rendering metrics to see whether geometry alone recommends the same poses.
  • My inference: the shared-calibration step assumes a fixed sensor rig across a dataset, so datasets captured with multiple vehicles, like Waymo, are forced into per-sequence calibration; estimating per-sequence extrinsic priors from odometry would make the pipeline scale to fleet-scale data.
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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

5 major / 6 minor

Summary. This paper proposes a NeRF-based optimization pipeline that refines vehicle trajectories and extrinsic sensor calibration for four public autonomous driving datasets (KITTI-360, NuScenes, PandaSet, Waymo). The pipeline builds on the authors' MOISST and SOAC models, adds dynamic-object filtering and subsequence creation, and optimizes an extrinsic-correction embedding and a continuous trajectory-correction MLP. The authors evaluate the corrected poses against the original poses with three families of proxy metrics: COLMAP keypoint reprojection/track length, novel-view-synthesis PSNR/SSIM/LPIPS on NeRF-LiDAR, Nerfacto, and Splatfacto, and Delaunay-mesh-to-LiDAR precision/distance. They report that MOISST generally improves these metrics and release the optimized poses.

Significance. The intended contribution—public corrected sensor poses for widely used driving benchmarks—is potentially valuable to the community, and the paper includes several strengths: evaluation across four datasets, a KISS-ICP baseline for NuScenes, and independent Nerfstudio renderers for the NVS metrics. However, the central claim of 'significant improvements in sensor pose accuracy' overstates what the evidence shows. All reported metrics are internal-consistency measures, and the paper itself acknowledges in Section III-C.2 that NVS improvements are natural because the optimization minimizes the same photometric objective. The results also show regressions for SOAC on KITTI-360 and Waymo and only minimal MOISST gains on KITTI-360, so the claim of consistent improvement is not supported without error bars or significance tests. If the authors re-scope the claims and add an external validation (synthetic ground truth or a high-accuracy reference), the contribution could be solid.

major comments (5)
  1. [Abstract; Section V (Tables IIa–IIe)] The abstract's claim of 'significant improvements in sensor pose accuracy' is not established because every reported metric is a proxy for internal consistency rather than absolute pose accuracy. The COLMAP reprojection and mesh-to-LiDAR metrics are less circular than the NVS metrics, but they still cannot detect global distortions such as scale drift or datum changes that preserve self-consistency; the optimizer could improve alignment between sensors while moving the trajectory away from the physical one. I recommend adding a synthetic experiment with known pose perturbations (e.g., perturbing a rendered scene or using a simulator with ground-truth poses) and/or comparing against a high-accuracy external reference trajectory to demonstrate that the corrected poses are closer to the true trajectory.
  2. [Section V-A; Tables IIa and IIe; Fig. 9] The text claims a consistent improvement over original poses, but the data contradict this for SOAC: on KITTI-360, reprojection error worsens from 0.586 to 0.735 and mesh precision drops from 0.589 to 0.582; on Waymo, reprojection error worsens from 1.137 to 1.344 and precision drops from 0.441 to 0.417. For MOISST on KITTI-360, the gains are tiny (0.586 to 0.577 for reprojection; 6.53 to 6.68 for track length), and Fig. 9 shows reprojection improves in only 24 of 50 subsequences. The manuscript should report per-sequence distributions, confidence intervals, and a significance test, and should replace 'consistent improvement' with a more conditional statement.
  3. [Section III-C.2; Table IIb] The NVS evaluation is partially circular because the NeRF-LiDAR model used for evaluation is from the same MOISST family that produced the poses; PSNR/SSIM/LPIPS gains on this model can reflect better optimization of the same photometric objective rather than pose accuracy. The paper acknowledges this in Section III-C.2, but the related-work discussion in Section II-C promises an unbiased evaluation, which is stronger than the evidence. The Nerfacto and Splatfacto evaluations mitigate the renderer-specific concern, but all NVS metrics still reward photometric consistency. I suggest adding a structure-based evaluation that does not use the optimization objective, for example running a full COLMAP bundle adjustment initialized from the optimized poses and reporting reconstruction statistics, or evaluating a downstream geometric task such as monocular depth alignment.
  4. [Section V; Table III] The NuScenes comparison is confounded by KISS-ICP preprocessing. The baseline 'Original' lacks Z-axis information, while MOISST and SOAC are applied after KISS-ICP; Table III shows that KISS-ICP alone improves all metrics (e.g., LPIPS 0.465 to 0.418), so part of the reported MOISST/SOAC improvement is attributable to this preprocessing. To isolate the contribution of the NeRF-based optimization, report MOISST/SOAC applied directly to the original SE2 poses, or otherwise quantify the additive effect beyond the KISS-ICP baseline under matched experimental conditions.
  5. [Section V-E; Fig. 8] The sign-agreement analysis shows that the different metrics agree only moderately (over 0.5 overall, with the lowest agreement on KITTI-360). This means the method does not deliver a single demonstrably accurate pose set but rather candidate poses subject to a user-selected voting rule. The paper should either present a principled selection criterion or weaken the claim that the pipeline outputs improved poses.
minor comments (6)
  1. [Section IV-B] 'Subsequencies' should be spelled 'subsequences'.
  2. [Section V-C and Figures 13–16] 'Delauney' should be 'Delaunay' (also in the caption of Table IIe).
  3. [Figures 17–20] The caption label 'Imgine' appears to be a typo for 'Image'.
  4. [Section V-A] The text contains visible LaTeX artifacts such as '10%⁄tildelow36%'; please ensure the compiled PDF renders these ranges correctly.
  5. [Table I] The entries '15 epochsiterations' and '10 epochsNVS iterations' appear to be missing separators; clarify the intended units for epochs and iterations.
  6. [Section III-C.1] The phrase 'the average point reprojection error' should specify that the average is computed over the matched keypoints used in triangulation.

Circularity Check

1 steps flagged · score 4.0 of 10

NVS metrics mirror the NeRF photometric objective, but COLMAP and geometric metrics give partial independent support; moderate circularity.

  1. fitted input called prediction [Section III-C.2 (Pose accuracy assessment, Novel View Synthesis)]
    "As we optimize our poses through NeRF training, it appears natural that the NVS task should provide the most constant improvement in the metrics."

    The pose corrections Ecor and Tcor are optimized by backpropagating the NeRF photometric and geometric consistency loss into the poses (Section III-B.1). The NVS metrics (PSNR, SSIM, LPIPS) then measure rendering quality using the same kind of photometric consistency that was minimized during pose optimization. The paper itself concedes that the NVS improvement is 'natural' given the optimization, so presenting these metrics as evidence of improved pose accuracy is partly reporting the training objective under a new name. The held-out-frame split reduces, but does not eliminate, the circularity because the poses were optimized using the full sequence, including the frames later used as NVS evaluation views.

full rationale

The paper's central claim is that its NeRF-based optimization improves sensor pose accuracy, but no ground-truth poses are available. The evaluation therefore relies on proxy metrics. The clearest circular component is the Novel View Synthesis evaluation: the poses are fitted to minimize photometric consistency in a NeRF, and PSNR/SSIM/LPIPS measure photometric consistency in the same framework. The paper explicitly acknowledges this by saying that NVS improvement is 'natural' because the poses are optimized through NeRF training. This makes the NVS results partially self-confirmatory. However, the COLMAP reprojection and track-length metrics use SIFT keypoints and triangulation independent of the NeRF model, and the Delaunay mesh-to-LiDAR metrics compare camera-derived geometry against LiDAR point clouds; these provide partial external evidence that the optimized poses improve multi-view and cross-sensor consistency. The paper also compares against Nerfstudio pose optimization and, for NuScenes, against KISS-ICP, showing that the improvements are not solely an artifact of replacing the original poses with a generic SLAM-like preprocessing step. The lack of any absolute ground-truth trajectory means all metrics remain internal-consistency measures, and a global distortion such as scale drift could go undetected, but that is a correctness limitation rather than a definitional circularity. The self-citations to MOISST and SOAC are used as the optimization backbone, yet the evaluation does not reduce entirely to those prior papers' claims: the COLMAP and geometric metrics would still be meaningful if the optimized poses were worse. Overall, the circularity is moderate and localized mainly to the NVS metrics, so the paper retains independent content in its other evaluation axes.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper does not introduce new physical entities or theoretical constructs. It relies on standard NeRF machinery, on the authors' existing MOISST and SOAC models, and on a set of heuristically chosen hyperparameters. The most consequential assumptions are the static-sensor-setup assumption and the use of partially circular evaluation metrics.

free parameters (3)
  • Image downscale factors for MOISST and SOAC = KITTI-360: MOISST 2, SOAC 4; NuScenes: 4 and 6; PandaSet: 4 and 6; Waymo: 4 and 6
    Chosen by hand to balance training time and reconstruction quality; they affect which details the NeRF can model and therefore the optimized poses.
  • Subsequence length and selection = KITTI-360 and Waymo: 50 meters; NuScenes: 30 meters; PandaSet: first 10 sequences with semantic labels
    Selected heuristically to keep NeRF training tractable. The choice determines how much of the scene each model sees and could influence pose refinement quality.
  • Optimization iterations = 15 epochs for calibration and trajectory optimization, 10 epochs for NeRF-LiDAR NVS, 30k steps for Nerfstudio models
    Fixed across datasets; no convergence analysis or sensitivity study is provided, so the effect on the results is not quantified.
assumptions (4)
  • domain assumption The transformation between sensors remains static across all sequences in a dataset, so a shared extrinsic calibration embedding can be optimized.
    Stated in Section III-A. The paper relaxes this for Waymo by optimizing per sequence, acknowledging that different capture vehicles have different calibration.
  • domain assumption Dynamic objects can be removed by reprojecting enlarged bounding boxes, so the remaining scene is static enough for NeRF optimization.
    Dynamic filtering in Section III-A depends on the provided bounding boxes and the assumption that enlarged boxes fully cover moving objects without removing too much static geometry.
  • domain assumption The proxy metrics, especially novel view synthesis metrics, indicate pose accuracy even though they share the objective being optimized.
    The entire evaluation in Section III-C relies on this. The paper acknowledges the risk in III-C.2, but still uses NVS metrics as evidence of pose improvement.
  • domain assumption KISS-ICP provides a reasonable Z-axis initialization for NuScenes before MOISST and SOAC optimization.
    Section V states that NuScenes poses lack Z-axis information and that KISS-ICP is used first. The fairness comparison in Table III shows ICP alone improves less than MOISST or SOAC, but the validity of the initialization is inherited from KISS-ICP.

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Pith. "Pith review of Pose Optimization for Autonomous Driving Datasets using Neural Rendering Models." pith.science (2026). https://pith.science/paper/OHQMCSC5

@misc{pith2026250415776,
  author       = {Pith},
  title        = {Pith review of: Pose Optimization for Autonomous Driving Datasets using Neural Rendering Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OHQMCSC5}},
  note         = {Machine review of arXiv:2504.15776}
}
read the original abstract

Autonomous driving systems rely on accurate perception and localization of the ego car to ensure safety and reliability in challenging real-world driving scenarios. Public datasets play a vital role in benchmarking and guiding advancement in research by providing standardized resources for model development and evaluation. However, potential inaccuracies in sensor calibration and vehicle poses within these datasets can lead to erroneous evaluations of downstream tasks, adversely impacting the reliability and performance of the autonomous systems. To address this challenge, we propose a robust optimization method based on Neural Radiance Fields (NeRF) to refine sensor poses and calibration parameters, enhancing the integrity of dataset benchmarks. To validate improvement in accuracy of our optimized poses without ground truth, we present a thorough evaluation process, relying on reprojection metrics, Novel View Synthesis rendering quality, and geometric alignment. We demonstrate that our method achieves significant improvements in sensor pose accuracy. By optimizing these critical parameters, our approach not only improves the utility of existing datasets but also paves the way for more reliable autonomous driving models. To foster continued progress in this field, we make the optimized sensor poses publicly available, providing a valuable resource for the research community.

Figures

Figures reproduced from arXiv: 2504.15776 by the authors.

Figure 1
Figure 1. Dataset pose improvement: Left, rendering comparison between original poses and optimized poses on Waymo open dataset [1] (from left to right: normal map, depth map and RGB rendering). Right, the changes in metrics between the original poses (in blue) and the poses optimized with MOISST (in red) for each dataset, grouped in 3 categories: Novel View Synthesis, Structure-from-Motion, Geometric. Abstract—Autonomous dri… view at source ↗
Figure 2
Figure 2. Pipeline: First the data is preprocessed to remove dynamic elements, and create subsequences fitting our NeRF models. Then, the extrinsic parameters are optimized, followed by the trajectories. Finally, the new poses are evaluated. each individual NeRF, while considering the overlap between sensors, which results in more reliable conver￾gence to the correct calibration. Similarly to MOISST, we consider the system sy… view at source ↗
Figure 3
Figure 3. NuScenes NeRF-LiDAR Novel View Synthesis [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: NuScenes NeRF-LiDAR Novel View Synthesis normal maps [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: PandaSet Nerfstudio Novel View Synthesis [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 7
Figure 7. Figure 7: Examples of challenging cases [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 6
Figure 6. Figure 6: Delauney mesh on NuScenes sequence. (a) It is very dark inside the tunnel, with a bright light at the end. The model is unable to find distinctive features. (b) The scene exhibits highly repetitive elements, such as the green grass, and includes distant structures beyo…
Figure 8
Figure 8. Figure 8: Sign agreement matrices: on all the metrics for each dataset. (1) Colmap reprojection, (2) Colmap track length (3) MOISST* PSNR (4) MOISST* SSIM (5) MOISST* LPIPS (6) Nerfacto PSNR (7) Nerfacto SSIM (8) Nerfacto LPIPS (9) Splatfacto PSNR (10) Splatfacto SSIM (11) Splat…
Figure 9
Figure 9. Figure 9: KITTI-360 Colmap metrics vs original poses [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 11
Figure 11. Figure 11: Pandaset Colmap metrics vs original poses [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 13
Figure 13. Figure 13: KITTI-360 Delauney metrics vs original poses [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]
Figure 15
Figure 15. Figure 15: Pandaset Delauney metrics vs original poses [PITH_FULL_IMAGE:figures/full_fig_p015_15.png]
Figure 17
Figure 17. Figure 17: KITTI-360 NVS metrics vs original poses (improvement in [PITH_FULL_IMAGE:figures/full_fig_p016_17.png]
Figure 18
Figure 18. Figure 18: NuScenes NVS metrics vs original poses (improvement in [PITH_FULL_IMAGE:figures/full_fig_p016_18.png]
Figure 19
Figure 19. Figure 19: Pandaset NVS metrics vs original poses (improvement in [PITH_FULL_IMAGE:figures/full_fig_p016_19.png]
Figure 20
Figure 20. Figure 20: Waymo NVS metrics vs original poses (improvement in [PITH_FULL_IMAGE:figures/full_fig_p016_20.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.