5 citations · 8 across the 11 of their papers we have counts for
6 papers · 1 filter
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with -NeuS
Haoran Zhang, Junkai Deng, Xuhui Chen +5
Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. R…
Quasi-Medial Distance Field (Q-MDF): A Robust Method for Approximating and Discretizing Neural Medial Axes
Jiayi Kong, Chen Zong, Jun Luo +5
The medial axis, a lower-dimensional descriptor that captures the extrinsic structure of a shape, plays an important role in digital geometry processing. Despite its importance, co…
A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions
Jiangbei Hu, Yanggeng Li, Fei Hou +5
Unsigned distance fields (UDFs) provide a versatile framework for representing a diverse array of 3D shapes, encompassing both watertight and non-watertight geometries. Traditional…
GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lighting
Jiaze Li, Zhengyu Wen, Luo Zhang +4
The 3D Gaussian Splatting technique has significantly advanced the construction of radiance fields from multi-view images, enabling real-time rendering. While point-based rasteriza…
Diffusing Winding Gradients (DWG): A Parallel and Scalable Method for 3D Reconstruction from Unoriented Point Clouds
Weizhou Liu, Jiaze Li, Xuhui Chen +6
This paper presents a new method, Diffusing Winding Gradients (DWG), for reconstructing watertight 3D surfaces from unoriented point clouds. Our method exploits the alignment betwe…
Topology-Aware Latent Diffusion for 3D Shape Generation
Jiangbei Hu, Ben Fei, Baixin Xu +6
We introduce a new generative model that combines latent diffusion with persistent homology to create 3D shapes with high diversity, with a special emphasis on their topological ch…