1 citations · 2 across the 11 of their papers we have counts for
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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…