MagnetGS-Mesh: High-Quality Multi-Object Mesh Reconstruction via Adaptive Surface Optimization

We present MagnetGS-Mesh, an integrated framework thatdirectly recovers high-quality, object-wise surface meshes from 3D Gaus-sian Splatting (3DGS) while preserving photorealistic novel-view syn-thesis. Prior 3DGS-to-mesh methods generate meshes by considering allGaussians without distinguishing objects from the background, whichinevitably yields excessive and unnecessary vertices in irrelevant regions.To address this efficiency bottleneck, we adopt an object-wise reconstruc-tion approach. However, decomposing 3DGS representations into indi-vidual objects introduces distinct geometric challenges: numerous out-lier Gaussians appear far from true surfaces, and the remaining surfaceGaussians often exhibit highly non-uniform density. Since robust meshextraction fundamentally requires a uniform distribution of Gaussiansto prevent surface holes and artifacts, these irregularities severely de-grade geometric fidelity. To resolve these issues, we propose a geometry-aligned two-stage training procedure. In Stage 1, we employ EnhancedOccupancy Learning to produce crisp boundaries, ensuring accurate 3Dsegmentation. In Stage 2, to address the uneven distribution, we in-troduce the Adaptive Local Normal Consistency (ALNC) Loss.This mechanism acts as a “magnet,” strategically relocating outlier Gaus-sians to sparse surface regions to ensure uniform coverage. By adap-tively aligning these relocated Gaussians with local surface normals,ALNC effectively fills geometric gaps while maintaining structural co-herence. Experimentally, MagnetGS-Mesh achieves competitive or supe-rior mesh quality compared to state-of-the-art methods while simultane-ously reducing model size by substantial margins. The code is availableat https://github.com/MinsuPark0752/MagnetGS-Mesh.