  Point cloud registration is a fundamental problem for large-scale 3D scene
scanning and reconstruction. With the help of deep learning, registration
methods have evolved significantly, reaching a nearly-mature stage. As the
introduction of Neural Radiance Fields (NeRF), it has become the most popular
3D scene representation as its powerful view synthesis capabilities. Regarding
NeRF representation, its registration is also required for large-scale scene
reconstruction. However, this topic extremly lacks exploration. This is due to
the inherent challenge to model the geometric relationship among two scenes
with implicit representations. The existing methods usually convert the
implicit representation to explicit representation for further registration.
Most recently, Gaussian Splatting (GS) is introduced, employing explicit 3D
Gaussian. This method significantly enhances rendering speed while maintaining
high rendering quality. Given two scenes with explicit GS representations, in
this work, we explore the 3D registration task between them. To this end, we
propose GaussReg, a novel coarse-to-fine framework, both fast and accurate. The
coarse stage follows existing point cloud registration methods and estimates a
rough alignment for point clouds from GS. We further newly present an
image-guided fine registration approach, which renders images from GS to
provide more detailed geometric information for precise alignment. To support
comprehensive evaluation, we carefully build a scene-level dataset called
ScanNet-GSReg with 1379 scenes obtained from the ScanNet dataset and collect an
in-the-wild dataset called GSReg. Experimental results demonstrate our method
achieves state-of-the-art performance on multiple datasets. Our GaussReg is 44
times faster than HLoc (SuperPoint as the feature extractor and SuperGlue as
the matcher) with comparable accuracy.
