  We introduce an integrated precise LiDAR, Inertial, and Visual (LIV)
multimodal sensor fused mapping system that builds on the differentiable
\pre{surface splatting }\now{Gaussians} to improve the mapping fidelity,
quality, and structural accuracy. Notably, this is also a novel form of tightly
coupled map for LiDAR-visual-inertial sensor fusion.
  This system leverages the complementary characteristics of LiDAR and visual
data to capture the geometric structures of large-scale 3D scenes and restore
their visual surface information with high fidelity. The initialization for the
scene's surface Gaussians and the sensor's poses of each frame are obtained
using a LiDAR-inertial system with the feature of size-adaptive voxels. Then,
we optimized and refined the Gaussians using visual-derived photometric
gradients to optimize their quality and density.
  Our method is compatible with various types of LiDAR, including solid-state
and mechanical LiDAR, supporting both repetitive and non-repetitive scanning
modes. Bolstering structure construction through LiDAR and facilitating
real-time generation of photorealistic renderings across diverse LIV datasets.
It showcases notable resilience and versatility in generating real-time
photorealistic scenes potentially for digital twins and virtual reality, while
also holding potential applicability in real-time SLAM and robotics domains.
  We release our software and hardware and self-collected datasets to benefit
the community.
