arXiv Computer Vision
Oct 2

HierGF: Hierarchical Gaussian Fields via Geometry-perception Message Passing for Sparse-view 3D Reconstruction

The paper introduces HierGF, a method for sparse‑view 3D reconstruction that uses a hierarchical geometry‑perception backbone to generate structured pseudo‑supervision from limited views. It incorporates a learnable confidence network to focus gradients on cross‑view consistent content and a geometrically consistent densification module to improve alignment and fill under‑sampled regions. This approach aims to overcome the challenges of few images and incomplete coverage that typically lead to overfitting and structural holes in existing methods.

By Bi'an Du, Zhimin Zhang, Daizong Liu, Baoquan Chen, Wei Hu
Hugging Face Trending Papers
4d ago

ACD-GS: Asymmetric Curvature-aware Densification for 3D Gaussian Splatting

ACD-GS introduces an Asymmetric Curvature-aware Densification framework for 3D Gaussian Splatting that uses second-order depth responses to guide cloning and splitting, thereby better capturing geometric discontinuities. It incorporates a multi-view photometric consistency constraint and a capacity-aware adaptive regulation strategy to balance densification and pruning during training. The approach reduces Gaussian count and storage by nearly 60% while maintaining competitive rendering fidelity, outperforming existing methods across multiple datasets.