1 citations · 2 across the 11 of their papers we have counts for
12 papers
Metric--Phase Fields: Decoupling Distance and Sign for Thin-Structure Reconstruction from Unoriented Point Clouds
Jiayi Kong, Xuhui Chen, Chen Zong +4
Neural Signed Distance Functions (SDFs) excel at reconstructing watertight manifolds but fail on thin structures and open boundaries due to strict inside--outside constraints. Conv…
FlexPara: Flexible Neural Surface Parameterization
Yuming Zhao, Qijian Zhang, Junhui Hou +3
Surface parameterization is a fundamental geometry processing task, laying the foundations for the visual presentation of 3D assets and numerous downstream shape analysis scenarios…
SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation
Qijian Zhang, Xiaozheng Jian, Xuan Zhang +2
Conventional production workflow of high-precision mesh assets necessitates a cumbersome and laborious process of manual sculpting by specialized 3D artists/modelers. The recent ye…
GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction
Jiepeng Wang, Yuan Liu, Peng Wang +5
3D Gaussian Splatting has achieved impressive performance in novel view synthesis with real-time rendering capabilities. However, reconstructing high-quality surfaces with fine det…
Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds
Chuanxiang Yang, Yuanfeng Zhou, Guangshun Wei +4
As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capab…
MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors
Qingming Liu, Yuan Liu, Jiepeng Wang +4
In this paper, we propose MoDGS, a new pipeline to render novel views of dy namic scenes from a casually captured monocular video. Previous monocular dynamic NeRF or Gaussian Splat…
Flatten Anything: Unsupervised Neural Surface Parameterization
Qijian Zhang, Junhui Hou, Wenping Wang +1
Surface parameterization plays an essential role in numerous computer graphics and geometry processing applications. Traditional parameterization approaches are designed for high-q…
DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction
Yuxin Yao, Siyu Ren, Junhui Hou +3
This paper explores the problem of reconstructing temporally consistent surfaces from a 3D point cloud sequence without correspondence. To address this challenging task, we propose…
Dynamic 3D Point Cloud Sequences as 2D Videos
Yiming Zeng, Junhui Hou, Qijian Zhang +2
Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in b…
DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fields
Siyu Ren, Junhui Hou, Xiaodong Chen +2
Qualifying the discrepancy between 3D geometric models, which could be represented with either point clouds or triangle meshes, is a pivotal issue with board applications. Existing…
On Optimal Sampling for Learning SDF Using MLPs Equipped with Positional Encoding
Guying Lin, Lei Yang, Yuan Liu +6
Neural implicit fields, such as the neural signed distance field (SDF) of a shape, have emerged as a powerful representation for many applications, e.g., encoding a 3D shape and pe…
Cross-BERT for Point Cloud Pretraining
Xin Li, Peng Li, Zeyong Wei +7
Introducing BERT into cross-modal settings raises difficulties in its optimization for handling multiple modalities. Both the BERT architecture and training objective need to be ad…