# Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering

arXiv: https://arxiv.org/abs/2504.15776 · HTML: https://mlsanity.com/paper/2504.15776/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 24 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving](https://mlsanity.com/paper/2112.12610/) (2021)
- [NeRF--: Neural Radiance Fields Without Known Camera Parameters](https://mlsanity.com/paper/2102.07064/) (2021)
- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving](https://mlsanity.com/paper/2311.16945/) (2023)
- [MOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration](https://mlsanity.com/paper/2303.03056/) (2023)
- [SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields](https://mlsanity.com/paper/2311.15803/) (2023)
- [3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration](https://mlsanity.com/paper/2403.11577/) (2024)
- [Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy](https://mlsanity.com/paper/2109.03462/) (2021)
- [UniCal: Unified Neural Sensor Calibration](https://mlsanity.com/paper/2409.18953/) (2024)
- [Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles](https://mlsanity.com/paper/2408.11196/) (2024)
- [Continuous Pose for Monocular Cameras in Neural Implicit Representation](https://mlsanity.com/paper/2311.17119/) (2023)
- [ViiNeuS: Volumetric Initialization for Implicit Neural Surface reconstruction of urban scenes with limited image overlap](https://mlsanity.com/paper/2403.10344/) (2024)
- [Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting](https://mlsanity.com/paper/2504.04597/) (2025)

## Built on by (most cited first, 4 of 4)

- [UniQueR: Unified Query-based Feedforward 3D Reconstruction](https://mlsanity.com/paper/2603.22851/) (2026)
- [Beyond the Vehicle: Cooperative Localization by Fusing Point Clouds for GPS-Challenged Urban Scenarios](https://mlsanity.com/paper/2602.03908/) (2026)
- [SpectralSplat: Appearance-Disentangled Feed-Forward Gaussian Splatting for Driving Scenes](https://mlsanity.com/paper/2604.03462/) (2026)
- [DrivingVoxels: Compositional Sparse Voxel Rasterization for Dynamic Driving Scene Reconstruction](https://mlsanity.com/paper/2606.23031/) (2026)

## Abstract (arXiv)

Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccuracies in sensor calibration and ego-poses can silently degrade both model performance and evaluation reliability. We introduce MOISST++, a Neural Radiance Field (NeRF)-based pipeline that jointly refines extrinsic sensor calibration and continuous-time ego-trajectories at dataset scale. The method optimizes shared rig parameters across multiple subsequences and corrects per-subsequence trajectories via a learned continuous-time correction, going beyond prior work that targets individual scenes. We validate pose improvements without ground truth through a complementary evaluation suite combining Structure from Motion (SfM) triangulation, novel view synthesis, and multi-modal geometric consistency metrics, verify their coherence via cross-metric agreement, and confirm their sensitivity through a controlled-perturbation study with known injected errors. Applied to four major datasets (KITTI-360, nuScenes, PandaSet, and Waymo), MOISST++ yields statistically significant improvements on most metrics on nuScenes, PandaSet and Waymo, and marginal, within-noise changes on the already well-calibrated KITTI-360. We publicly release the optimized poses and calibration parameters, together with our evaluation code, to support more reliable research and benchmarking.