Structured-Li-GS: LiDAR-enhanced 3D Gaussians use 40% fewer primitives
Lightweight 3D reconstruction pipeline beats SOTA with accurate, dense LiDAR point clouds.
Researchers introduced Structured-Li-GS, a lightweight 3D Gaussian Splatting pipeline that integrates LiDAR-inertial-visual SLAM. By anchoring Gaussian primitives on sub-sampled point clouds and initializing ellipsoidal parameters from local surface geometry, the method achieves high-quality 3D reconstructions with fewer Gaussians. Training incorporates photometric, flattening, offset, depth, and normal losses without Gaussian densification. Tested on benchmark datasets and a custom hardware-synchronized LiDAR-camera scanner, it surpasses state-of-the-art methods in quality and efficiency.
- Anchors Gaussian primitives on sub-sampled LiDAR point clouds and initializes ellipsoidal parameters from local surface geometry, removing need for densification.
- Achieves up-to-scale, high-fidelity 3D reconstructions using 40% fewer Gaussians than state-of-the-art 3DGS methods.
- Custom hardware-synchronized LiDAR-camera handheld scanner used for real-world validation; outperforms baselines on both benchmark and in-house datasets.
Why It Matters
Enables efficient, high-quality 3D scene reconstruction for robotics and AR with fewer resources.