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3D LiDAR Mapping in Dynamic Environments using a 4D Implicit Neural Representation
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Proposes a 4D implicit neural map that fits a time-dependent TSDF per point with sparse feature grids, a shared MLP decoder and DCT basis functions, trained unsupervised from LiDAR scans to build static maps and segment dynamic points.
Problem
Building accurate 3D maps of dynamic environments from LiDAR scan sequences while removing moving objects, without semantic labels.
Approach
Encode each point's time-varying TSDF as a weighted sum of globally shared temporal basis functions whose location-dependent weights come from a multi-resolution hash feature grid and MLP decoder; the first (static) basis weight gives the static map and is thresholded to label dynamic points. A piecewise surface/free-space loss with numerical Eikonal regularization supervises it without ground-tru
Key results
- On ToyCar3, the method achieves Chamfer-L1 of 0.452 cm and F-score 98.35%, best among VDB-fusion, NKSR and SHINE-mapping.
- On KTH-Dynamic-Benchmark, it reaches the best associated accuracy on KITTI 00 (98.97), KITTI 05 (98.95) and Argoverse2 (97.53).
- On Newer College, Chamfer-L1 is 6.17 cm, behind SHINE-mapping (6.33) only in accuracy, while completion is best at 5.85 cm.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the 4D implicit neural representation for static mapping instead of VDB-fusion | 4D implicit neural representation · ToyCar3 | completion (cm): 0.574 -> 0.438 | Helped |
| Use the 4D implicit neural representation for static mapping instead of VDB-fusion | 4D implicit neural representation · ToyCar3 | accuracy (cm): 0.481 -> 0.468 | Helped |
| Use the 4D implicit neural representation for static mapping instead of VDB-fusion | 4D implicit neural representation · ToyCar3 | Chamfer-L1 (cm): 0.528 -> 0.452 | Helped |
| Use the 4D implicit neural representation for static mapping instead of VDB-fusion | 4D implicit neural representation · ToyCar3 | F-score (%): 97.95 -> 98.35 | Helped |
| Use the 4D implicit neural representation for static mapping instead of SHINE-mapping | 4D implicit neural representation · Newer College | completion (cm): 6.80 -> 5.85 | Helped |
| Use the 4D implicit neural representation for static mapping instead of SHINE-mapping | 4D implicit neural representation · Newer College | accuracy (cm): 5.86 -> 6.49 | Hurt |
| Use the 4D implicit neural representation for static mapping instead of SHINE-mapping | 4D implicit neural representation · Newer College | Chamfer-L1 (cm): 6.33 -> 6.17 | Helped |
| Use the 4D implicit neural representation for static mapping instead of SHINE-mapping | 4D implicit neural representation · Newer College | F-score (%): 97.67 -> 97.50 | Hurt |
| Use the 4D implicit neural representation for dynamic object segmentation instead of Octomap* | 4D implicit neural representation · KITTI Seq. 00 | AA: 95.83 -> 98.97 | Helped |
| Use the 4D implicit neural representation for dynamic object segmentation instead of Octomap* | 4D implicit neural representation · KITTI Seq. 05 | AA: 93.01 -> 98.95 | Helped |
| Use the 4D implicit neural representation for dynamic object segmentation instead of MapMOS | 4D implicit neural representation · Argoverse2 | AA: 92.65 -> 97.53 | Helped |
| Use the 4D implicit neural representation for dynamic object segmentation instead of Octomap | 4D implicit neural representation · Semi-Indoor | AA: 85.51 -> 82.79 | Hurt |
Surprising findings
- Accuracy is worse than SHINE-mapping and VDB-Fusion on noisy Newer College data because the method treats every scan as accurate.
- Semi-indoor sequence dynamic-object removal is weaker, with AA of 82.79 versus baselines such as Octomap* at 84.34.
Limitations
- Relies on poses from a separate SLAM/odometry method rather than estimating them jointly.
- Offline training only; cannot run online.
- Sensitive to measurement noise because every scan is treated as accurate.
Experimental setup
Datasets: ToyCar3 (Co-Fusion) Newer College KITTI Argoverse2 KTH-Dynamic-Benchmark
Benchmarks: ToyCar3 Newer College KTH-Dynamic-Benchmark
Metrics: completion accuracy Chamfer-L1 F-score static accuracy (SA) dynamic accuracy (DA) associated accuracy (AA)
Baselines: VDBfusion NKSR SHINE-mapping Erasor Removert Octomap 4DMOS MapMOS
Builds on: Instant-NGP Neuralangelo Li et al. neural scene flow / trajectory basis representation SHINE-mapping Eikonal regularization
Closest papers by meaning
- LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis (CVPR 2024 · similarity 0.90)
- TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception (NeurIPS 2025 · similarity 0.87)
- STGC-NeRF: Spatial-Temporal Geometric Consistency for LiDAR Neural Radiance Fields in Dynamic Scenes (AAAI 2025 · similarity 0.87)
- Swift-Mapping: Online Neural Implicit Dense Mapping in Urban Scenes (AAAI 2024 · similarity 0.87)
- Dynamic LiDAR Re-simulation using Compositional Neural Fields (CVPR 2024 · similarity 0.86)
- DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors (AAAI 2024 · similarity 0.86)
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