NAP3D: NeRF Assisted 3D-3D Pose Alignment for Autonomous Vehicles

• URL: http://arxiv.org/abs/2512.15080v1
• Date: Wed, 17 Dec 2025 04:56:38 GMT
• Title: NAP3D: NeRF Assisted 3D-3D Pose Alignment for Autonomous Vehicles
• Abstract summary: This work introduces NeRF-Assisted 3D-3D Pose Alignment (NAP3D), a complementary approach that leverages 3D-3D correspondences between the agent's current depth image and a pre-trained Neural Radiance Field (NeRF)<n>NAP3D refines the estimated pose even from novel viewpoints, without relying on revisiting previously observed locations.<n> Experiments demonstrate that NAP3D achieves camera pose correction within 5 cm on a custom dataset, robustly outperforming a 2D-3D Perspective-N-Point baseline.
• Score: 1.1168121941015012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate localization is essential for autonomous vehicles, yet sensor noise and drift over time can lead to significant pose estimation errors, particularly in long-horizon environments. A common strategy for correcting accumulated error is visual loop closure in SLAM, which adjusts the pose graph when the agent revisits previously mapped locations. These techniques typically rely on identifying visual mappings between the current view and previously observed scenes and often require fusing data from multiple sensors. In contrast, this work introduces NeRF-Assisted 3D-3D Pose Alignment (NAP3D), a complementary approach that leverages 3D-3D correspondences between the agent's current depth image and a pre-trained Neural Radiance Field (NeRF). By directly aligning 3D points from the observed scene with synthesized points from the NeRF, NAP3D refines the estimated pose even from novel viewpoints, without relying on revisiting previously observed locations. This robust 3D-3D formulation provides advantages over conventional 2D-3D localization methods while remaining comparable in accuracy and applicability. Experiments demonstrate that NAP3D achieves camera pose correction within 5 cm on a custom dataset, robustly outperforming a 2D-3D Perspective-N-Point baseline. On TUM RGB-D, NAP3D consistently improves 3D alignment RMSE by approximately 6 cm compared to this baseline given varying noise, despite PnP achieving lower raw rotation and translation parameter error in some regimes, highlighting NAP3D's improved geometric consistency in 3D space. By providing a lightweight, dataset-agnostic tool, NAP3D complements existing SLAM and localization pipelines when traditional loop closure is unavailable.

Related papers

• Map-Det3D: Metric Feed-Forward 3D Reconstruction Prior for Multi-view 3D Object Detection from Streaming Inputs [62.865932175477035]
  Metric 3D object detection is a core capability for embodied agents, yet most reliable systems lean on depth sensors.<n>We propose Map-Det3D, an online multi-view 3D object detection model that brings detection directly into a 3D space reconstructed from RGB.
  arXiv  Detail & Related papers  (2026-08-12T15:33:42Z)
• Selfi: Self Improving Reconstruction Engine via 3D Geometric Feature Alignment [15.822150318879052]
  We introduce Selfi, a self-improving 3D reconstruction pipeline via feature alignment.<n>We train a lightweight feature adapter using a reprojection-based consistency loss.<n>This enables state-of-the-art performance in both NVS and camera pose estimation.
  arXiv  Detail & Related papers  (2025-12-09T18:59:52Z)
• SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection [49.12928389918159]
  Existing monocular 3D detectors typically tame the pronounced nonlinear regression of 3D bounding box through decoupled prediction paradigm.<n>We propose novel Spatial-Projection Alignment (SPAN) with two pivotal components.<n>SPAN enforces an explicit global spatial constraint between the predicted and ground-truth 3D bounding boxes, thereby rectifying spatial drift caused by decoupled attribute regression.<n>3D-2D Projection Alignment ensures that the projected 3D box is aligned tightly within its corresponding 2D detection bounding box on the image plane, mitigating projection misalignment overlooked in previous works.
  arXiv  Detail & Related papers  (2025-11-10T04:48:48Z)
• UniPose: Unified Cross-modality Pose Prior Propagation towards RGB-D data for Weakly Supervised 3D Human Pose Estimation [14.52285662885727]
  We present UniPose, a unified cross-modality pose prior propagation method for weakly supervised 3D human pose estimation.<n>UniPose transfers 2D annotations from large-scale RGB datasets to the 3D domain via self-supervised learning.<n> Experiments on CMU Panoptic and ITOP datasets show that UniPose achieves comparable performance to fully supervised methods.
  arXiv  Detail & Related papers  (2025-09-27T15:49:30Z)
• TAPIP3D: Tracking Any Point in Persistent 3D Geometry [25.357437591411347]
  We introduce TAPIP3D, a novel approach for long-term 3D point tracking in monocular and RGB-D videos.<n>TAPIP3D represents videos as camera-stabilized feature clouds, leveraging depth and camera motion information.<n>Our 3D-centric formulation significantly improves performance over existing 3D point tracking methods.
  arXiv  Detail & Related papers  (2025-04-20T19:09:43Z)
• 3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction [50.07071392673984]
  Existing methods learn 3D rotations parametrized in the spatial domain using angles or quaternions. We propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression. Our method achieves state-of-the-art results on benchmarks such as ModelNet10-SO(3) and PASCAL3D+.
  arXiv  Detail & Related papers  (2024-11-01T12:50:38Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• Neural Voting Field for Camera-Space 3D Hand Pose Estimation [106.34750803910714]
  We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation. We propose a novel unified 3D dense regression scheme to estimate camera-space 3D hand pose via dense 3D point-wise voting in camera frustum.
  arXiv  Detail & Related papers  (2023-05-07T16:51:34Z)
• Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
  Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
  arXiv  Detail & Related papers  (2021-07-08T15:19:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.