Graph-based Point Cloud Surface Reconstruction using B-Splines
Generating continuous surfaces from discrete point cloud data is a fundamental task in several 3D vision applications. Real-world point clouds are inherently noisy due to various technical and environmental factors. Existing data-driven surface reconstruction algorithms rely heavily on ground truth normals or compute approximate normals as an intermediate step. This dependency makes them extremely unreliable for noisy point cloud datasets, even if the availability of ground truth training data is ensured, which is not always the case. B-spline reconstruction techniques provide compact surface representations of point clouds and are especially known for their smoothening properties. However, the complexity of the surfaces approximated using B-splines is directly influenced by the number and location of the spline control points. Existing spline-based modeling methods predict the locations of a fixed number of control points for a given point cloud, which makes it very difficult to match the complexity of its underlying surface. In this work, we develop a Dictionary-Guided Graph Convolutional Network-based surface reconstruction strategy where we simultaneously predict both the location and the number of control points for noisy point cloud data to generate smooth surfaces without the use of any point normals. We compare our reconstruction method with several well-known as well as recent baselines by employing widely-used evaluation metrics, and demonstrate that our method outperforms all of them both qualitatively and quantitatively.
Code (0)
등록된 구현이 없습니다.
Tasks
Point CloudsSimilar Papers 제목 키워드 기반
Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors
It is vital to infer signed distance functions (SDFs) from 3D point clouds. The latest methods rely on generalizing the priors learned from large scale supervision. However, the learned priors do not generalize well to v…
Surface ReconstructionLearning Modified Indicator Functions for Surface Reconstruction
Surface reconstruction is a fundamental problem in 3D graphics. In this paper, we propose a learning-based approach for implicit surface reconstruction from raw point clouds without normals. Our method is inspired by Gau…
LEMMASurface ReconstructionPoint Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction
The reconstruction of real-world surfaces is on high demand in various applications. Most existing reconstruction approaches apply 3D scanners for creating point clouds which are generally sparse and of low density. Thes…
point cloud upsamplingSurface ReconstructionDeep Point Cloud Simplification for High-quality Surface Reconstruction
The growing size of point clouds enlarges consumptions of storage, transmission, and computation of 3D scenes. Raw data is redundant, noisy, and non-uniform. Therefore, simplifying point clouds for achieving compact, cle…
Scene UnderstandingSurface ReconstructionVocal Bursts Intensity PredictionA Conditional Generative Adversarial Network for Rendering Point Clouds
In computer graphics, point clouds from laser scanning devices are difficult to render into photo-realistic images due to lack of information they carry about color, normal, lighting, and connection between points. Rende…
Generative Adversarial NetworkSurface Reconstruction