SF-Recon: Revolutionizing Lightweight Building Reconstruction with 3D Gaussian Splatting
By Zihan Li, Tengfei Wang, Wentian Gan, Hao Zhan, Xin Wang, Zongqian Zhan
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CV updates on arXiv.org.
Summary
SF-Recon introduces an innovative method for directly reconstructing lightweight building surface models from multi-view images, bypassing the cumbersome and quality-sensitive post-hoc mesh simplification steps of traditional multi-view geometry pipelines. The process begins by training an initial 3D Gaussian Splatting (3DGS) field to achieve a view-consistent representation. This is followed by a normal-gradient-guided Gaussian optimization, which, combined with multi-view edge-consistency pruning, distills and sharpens building structures like roofs and walls without external supervision. Finally, a multi-view depth-constrained Delaunay triangulation converts the refined Gaussian field into a lightweight, structurally faithful building mesh. Experimental results on a new SF dataset demonstrate SF-Recon's ability to produce models with substantially fewer faces and vertices while maintaining computational efficiency.
Why It Matters
This research represents a significant leap forward for professionals involved in digital city creation, urban planning, navigation, and fast geospatial analytics. Traditional methods for 3D building reconstruction are often bottlenecked by computationally intensive post-processing steps like mesh simplification, which can also compromise structural fidelity and introduce artifacts. SF-Recon's "simplification-free" approach, leveraging the power of 3D Gaussian Splatting, fundamentally streamlines the entire pipeline. This translates to a dramatically faster and more efficient generation of high-quality, lightweight 3D building models.
For AI professionals and those in related industries, this innovation means more agile data generation for large-scale digital twin initiatives, improved performance for real-time 3D applications, and a significant reduction in the computational overhead associated with managing complex urban environments. The ability to directly reconstruct structurally faithful models from raw imagery, without the need for manual cleanup or complex simplification algorithms, democratizes access to high-fidelity 3D geospatial data. This aligns with a broader industry trend towards developing end-to-end, intelligent computer vision pipelines, paving the way for more sophisticated AI-driven urban simulations, smart city applications, and environmental analysis where accurate, lightweight 3D models are paramount.