English

Hybrid Mesh-Gaussian Representation for Efficient Indoor Scene Reconstruction

Computer Vision and Pattern Recognition 2025-06-10 v1

Abstract

3D Gaussian splatting (3DGS) has demonstrated exceptional performance in image-based 3D reconstruction and real-time rendering. However, regions with complex textures require numerous Gaussians to capture significant color variations accurately, leading to inefficiencies in rendering speed. To address this challenge, we introduce a hybrid representation for indoor scenes that combines 3DGS with textured meshes. Our approach uses textured meshes to handle texture-rich flat areas, while retaining Gaussians to model intricate geometries. The proposed method begins by pruning and refining the extracted mesh to eliminate geometrically complex regions. We then employ a joint optimization for 3DGS and mesh, incorporating a warm-up strategy and transmittance-aware supervision to balance their contributions seamlessly.Extensive experiments demonstrate that the hybrid representation maintains comparable rendering quality and achieves superior frames per second FPS with fewer Gaussian primitives.

Keywords

Cite

@article{arxiv.2506.06988,
  title  = {Hybrid Mesh-Gaussian Representation for Efficient Indoor Scene Reconstruction},
  author = {Binxiao Huang and Zhihao Li and Shiyong Liu and Xiao Tang and Jiajun Tang and Jiaqi Lin and Yuxin Cheng and Zhenyu Chen and Xiaofei Wu and Ngai Wong},
  journal= {arXiv preprint arXiv:2506.06988},
  year   = {2025}
}
R2 v1 2026-07-01T03:05:20.179Z