Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration
Summary
A new framework addresses the challenge of accurate LiDAR-camera extrinsic calibration, a critical component for robust multi-modal perception. While targetless approaches avoid manual setup, they are often limited by a scarcity of discriminative cross-modal features. Existing methods using 3D Gaussian Splatting (3DGS) as a geometric proxy for extrinsic optimization tend to prioritize rendering quality, leading to geometric drift from the true LiDAR structure. The proposed method preserves the metric geometry of the Gaussian proxy by aggregating multi-view LiDAR observations for dense depth supervision. It also blocks photometric gradients from updating the Gaussian spatial parameters. This approach consistently outperforms existing targetless methods in calibration accuracy when validated on public driving datasets.
Key takeaway
For Computer Vision Engineers developing autonomous systems requiring precise multi-modal perception, this geometry-preserving 3D Gaussian Splatting framework offers a significant improvement in LiDAR-camera extrinsic calibration accuracy. If your current targetless calibration methods suffer from geometric drift, consider adopting techniques that prioritize metric geometry through dense depth supervision and controlled gradient updates. This can lead to more robust and reliable sensor fusion.
Key insights
Preserving 3D Gaussian Splatting's metric geometry via LiDAR depth supervision improves LiDAR-camera calibration accuracy.
Principles
- Prioritizing rendering quality can degrade geometric accuracy.
- Dense depth supervision enhances geometric proxy fidelity.
- Blocking photometric gradients prevents geometric drift.
Method
The method aggregates multi-view LiDAR observations for dense depth supervision and blocks photometric gradients from updating Gaussian spatial parameters to preserve metric geometry.
In practice
- Apply dense LiDAR depth supervision in 3DGS.
- Isolate geometric updates from photometric gradients.
- Use 3DGS for improved targetless calibration.
Topics
- LiDAR-Camera Calibration
- 3D Gaussian Splatting
- Multi-modal Perception
- Extrinsic Calibration
- Computer Vision
- Depth Supervision
Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Robotics Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.