CGC-GS: Cross geometric cues constrained Gaussian splatting

The planarized Gaussian representation, such as 2DGS, has shown great potential for geometry reconstruction. However, due to the lack of accurate geometric cues to evaluate the topology results and provide immediate feedback to the optimizer, they all fail to reconstruct the detailed geometry while maintaining high quality RGB rendering. This paper introduces the cross geometric cues that mixes the proposed scale-invariant monocular depth, confidence map-controlled normal prior and multi-view regularization consists of projection and photometric consistency to form the crossed constrain and evaluation of local topology in each optimization iteration, which results in more detailed geometric representation and perspective consistency. Moreover, a global density control strategy is proposed to correct the split standard and promote the homogeneous distribution of Gaussians in the whole scene, which benefits the high-frequency extraction ability and the removal of inappropriately large Gaussians. In experiments, the proposed method outperforms the baseline on overfitting and three datasets and achieves competitive results compared to other state-of-the-art (SOTA) methods. The relevant code will be be published at https://github.com/Zerui-Yu/CGC-GS.