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DiffLoc: Diffusion Model for Outdoor LiDAR Localization
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Proposes DiffLoc, a diffusion-based absolute pose regression method for outdoor LiDAR localization that uses DINOv2 features with a static-object-aware pool and a conditional pose denoiser.
Problem
Absolute pose regression (APR) for LiDAR localization lags behind structure-based 3D-3D correspondence methods, attributed to weak robust feature learning and lack of an iterative denoising process.
Approach
Formulates LiDAR localization as conditional pose generation: a DDIM-style diffusion denoiser conditioned on foundation-model (DINOv2) range-image features, with a static-object-aware attention pool that emphasizes static regions; also adds pose-variance uncertainty estimation and relative-pose geometric guidance during denoising.
Key results
- On NCLT, DiffLoc achieves 1.19m/2.31° average error, improving over HypLiLoc (1.95m/3.16°) by about 39%/26.9%.
- On Oxford, DiffLoc achieves 3.53m/0.72° average error, ranking first in orientation and second in position behind SGLoc (3.14m/1.88°).
- Geometric constraint-guided denoising lowers Oxford error to 3.36m/0.62°.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add foundation-model feature learning (ablation) | DiffLoc · Oxford Radar RobotCar | position error (m): 6.64 -> 4.80 | Helped |
| add SOAP to foundation-model features (ablation) | DiffLoc · Oxford Radar RobotCar | position error (m): 4.80 -> 4.15 | Helped |
| add iterative denoising (ablation) | DiffLoc · NCLT | position error (m): 3.07 -> 1.90 | Helped |
| add geometric constraint-guided denoising | DiffLoc · Oxford Radar RobotCar | position error (m): 3.57m -> 3.46m | Helped |
| add geometric constraint-guided denoising | DiffLoc · Oxford Radar RobotCar | orientation error (°): 0.68° -> 0.59° | Helped |
| increase denoising steps from 2 to 20 (ablation) | DiffLoc · Oxford Radar RobotCar | position error (m): 13.76 -> 3.50 | Helped |
| increase denoising steps from 4 to 15 (ablation) | DiffLoc · NCLT | position error (m): 1.57 -> 1.19 | Helped |
| remove RANSAC denoising (ablation) | SGLoc · Oxford Radar RobotCar | position error (m): 3.14m -> 22.04m | Hurt |
| remove RANSAC denoising (ablation) | SGLoc · NCLT | orientation error (°): 3.54° -> 56.05° | Hurt |
| replace HypLiLoc with DiffLoc | LiDAR localization · NCLT | position error (m): 1.95 -> 1.19 | Helped |
| replace HypLiLoc with DiffLoc | LiDAR localization · Oxford Radar RobotCar | orientation error (°): 1.05 -> 0.72 | Helped |
Surprising findings
- On Oxford, DiffLoc's position error (3.53m) is still behind SGLoc (3.14m), so it is not best across all metrics.
Limitations
- Accuracy depends on number of denoising steps; more steps add runtime, and it is slower than HypLiLoc.
- Relies on pregenerated moving-object masks from a pretrained SPVCNN for SOAP supervision.
- Position accuracy on Oxford trails SGLoc.
Experimental setup
Datasets: Oxford Radar RobotCar NCLT
Benchmarks: Oxford Radar RobotCar NCLT
Metrics: mean position error (m) mean orientation error (deg)
Baselines: PointNetVLAD DCP SGLoc PointLoc PosePN PosePN++ PoseMinkLoc PoseSOE STCLoc NIDALoc HypLiLoc
Builds on: DINOv2 DDIM DiffusionDet SGLoc HypLiLoc
Closest papers by meaning
- LightLoc: Learning Outdoor LiDAR Localization at Light Speed (CVPR 2025 · similarity 0.89)
- DiffLO: Semantic-Aware LiDAR Odometry with Diffusion-Based Refinement (CVPR 2025 · similarity 0.89)
- DiffuBox: Refining 3D Object Detection with Point Diffusion (NeurIPS 2024 · similarity 0.87)
- SOAR: Regression-based LiDAR Relocalization for UAVs (NeurIPS 2026 · similarity 0.86)
- Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion (CVPR 2024 · similarity 0.86)
- LiSA: LiDAR Localization with Semantic Awareness (CVPR 2024 · similarity 0.85)
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