Flatten Anything: Unsupervised Neural Surface Parameterization
- URL: http://arxiv.org/abs/2405.14633v1
- Date: Thu, 23 May 2024 14:39:52 GMT
- Title: Flatten Anything: Unsupervised Neural Surface Parameterization
- Authors: Qijian Zhang, Junhui Hou, Wenping Wang, Ying He,
- Abstract summary: We introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization.
Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information.
Our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies.
- Score: 76.4422287292541
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Surface parameterization plays an essential role in numerous computer graphics and geometry processing applications. Traditional parameterization approaches are designed for high-quality meshes laboriously created by specialized 3D modelers, thus unable to meet the processing demand for the current explosion of ordinary 3D data. Moreover, their working mechanisms are typically restricted to certain simple topologies, thus relying on cumbersome manual efforts (e.g., surface cutting, part segmentation) for pre-processing. In this paper, we introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization via learning point-wise mappings between 3D points on the target geometric surface and adaptively-deformed UV coordinates within the 2D parameter domain. To mimic the actual physical procedures, we ingeniously construct geometrically-interpretable sub-networks with specific functionalities of surface cutting, UV deforming, unwrapping, and wrapping, which are assembled into a bi-directional cycle mapping framework. Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information, thus significantly reducing the strict requirements for mesh quality and even applicable to unstructured point cloud data. More importantly, our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies, since its learning process adaptively finds reasonable cutting seams and UV boundaries. Extensive experiments demonstrate the universality, superiority, and inspiring potential of our proposed neural surface parameterization paradigm. The code will be publicly available.
Related papers
- InfoNorm: Mutual Information Shaping of Normals for Sparse-View Reconstruction [15.900375207144759]
3D surface reconstruction from multi-view images is essential for scene understanding and interaction.
Recent implicit surface representations, such as Neural Radiance Fields (NeRFs) and signed distance functions (SDFs) employ various geometric priors to resolve the lack of observed information.
We propose regularizing the geometric modeling by explicitly encouraging the mutual information among surface normals of highly correlated scene points.
arXiv Detail & Related papers (2024-07-17T15:46:25Z) - ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D Point Clouds [52.03819676074455]
ParaPoint is an unsupervised neural learning pipeline for achieving global free-boundary surface parameterization.
This work makes the first attempt to investigate neural point cloud parameterization that pursues both global mappings and free boundaries.
arXiv Detail & Related papers (2024-03-15T14:35:05Z) - Flexible Isosurface Extraction for Gradient-Based Mesh Optimization [65.76362454554754]
This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field.
We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives.
arXiv Detail & Related papers (2023-08-10T06:40:19Z) - Learning Neural Implicit Representations with Surface Signal
Parameterizations [14.835882967340968]
We present a neural network architecture that implicitly encodes the underlying surface parameterization suitable for appearance data.
Our model remains compatible with existing mesh-based digital content with appearance data.
arXiv Detail & Related papers (2022-11-01T15:10:58Z) - HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper
Surfaces [54.77683371400133]
We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations.
Experimental results show that our HSurf-Net achieves the state-of-the-art performance on the synthetic shape dataset.
arXiv Detail & Related papers (2022-10-13T16:39:53Z) - Deep Active Surface Models [60.027353171412216]
Active Surface Models have a long history of being useful to model complex 3D surfaces but only Active Contours have been used in conjunction with deep networks.
We introduce layers that implement them that can be integrated seamlessly into Graph Convolutional Networks to enforce sophisticated smoothness priors.
arXiv Detail & Related papers (2020-11-17T18:48:28Z) - Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images [64.53227129573293]
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.
We design neural networks capable of generating high-quality parametric 3D surfaces which are consistent between views.
Our method is supervised and trained on a public dataset of shapes from common object categories.
arXiv Detail & Related papers (2020-08-18T06:33:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.