12 research outputs found
Toward Controllable and Robust Surface Reconstruction from Spatial Curves
Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying algorithms of surface reconstruction to real-world cross-sections data. Lastly, we propose the Variational Implicit Point Set Surface which allows us to robustly handle noisy, sparse and non-uniform inputs, such as samples from spatial curves
Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points
Poisson surface reconstruction (PSR) remains a popular technique for
reconstructing watertight surfaces from 3D point samples thanks to its
efficiency, simplicity, and robustness. Yet, the existing PSR method and
subsequent variants work only for oriented points. This paper intends to
validate that an improved PSR, called iPSR, can completely eliminate the
requirement of point normals and proceed in an iterative manner. In each
iteration, iPSR takes as input point samples with normals directly computed
from the surface obtained in the preceding iteration, and then generates a new
surface with better quality. Extensive quantitative evaluation confirms that
the new iPSR algorithm converges in 5-30 iterations even with randomly
initialized normals. If initialized with a simple visibility based heuristic,
iPSR can further reduce the number of iterations. We conduct comprehensive
comparisons with PSR and other powerful implicit-function based methods.
Finally, we confirm iPSR's effectiveness and scalability on the AIM@SHAPE
dataset and challenging (indoor and outdoor) scenes. Code and data for this
paper are at https://github.com/houfei0801/ipsr
Alternately denoising and reconstructing unoriented point sets
We propose a new strategy to bridge point cloud denoising and surface
reconstruction by alternately updating the denoised point clouds and the
reconstructed surfaces. In Poisson surface reconstruction, the implicit
function is generated by a set of smooth basis functions centered at the
octnodes. When the octree depth is properly selected, the reconstructed surface
is a good smooth approximation of the noisy point set. Our method projects the
noisy points onto the surface and alternately reconstructs and projects the
point set. We use the iterative Poisson surface reconstruction (iPSR) to
support unoriented surface reconstruction. Our method iteratively performs iPSR
and acts as an outer loop of iPSR. Considering that the octree depth
significantly affects the reconstruction results, we propose an adaptive depth
selection strategy to ensure an appropriate depth choice. To manage the
oversmoothing phenomenon near the sharp features, we propose a
-projection method, which means to project the noisy points onto the
surface with an individual control coefficient for each point.
The coefficients are determined through a Voronoi-based feature detection
method. Experimental results show that our method achieves high performance in
point cloud denoising and unoriented surface reconstruction within different
noise scales, and exhibits well-rounded performance in various types of inputs.
The source code is available
at~\url{https://github.com/Submanifold/AlterUpdate}.Comment: Accepted by Computers & Graphics from CAD/Graphics 202
Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches
We present Strokes2Surface, an offline geometry reconstruction pipeline that
recovers well-connected curve networks from imprecise 4D sketches to bridge
concept design and digital modeling stages in architectural design. The input
to our pipeline consists of 3D strokes' polyline vertices and their timestamps
as the 4th dimension, along with additional metadata recorded throughout
sketching. Inspired by architectural sketching practices, our pipeline combines
a classifier and two clustering models to achieve its goal. First, with a set
of extracted hand-engineered features from the sketch, the classifier
recognizes the type of individual strokes between those depicting boundaries
(Shape strokes) and those depicting enclosed areas (Scribble strokes). Next,
the two clustering models parse strokes of each type into distinct groups, each
representing an individual edge or face of the intended architectural object.
Curve networks are then formed through topology recovery of consolidated Shape
clusters and surfaced using Scribble clusters guiding the cycle discovery. Our
evaluation is threefold: We confirm the usability of the Strokes2Surface
pipeline in architectural design use cases via a user study, we validate our
choice of features via statistical analysis and ablation studies on our
collected dataset, and we compare our outputs against a range of
reconstructions computed using alternative methods.Comment: 15 pages, 14 figure
Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
We propose Neural Gradient Learning (NGL), a deep learning approach to learn
gradient vectors with consistent orientation from 3D point clouds for normal
estimation. It has excellent gradient approximation properties for the
underlying geometry of the data. We utilize a simple neural network to
parameterize the objective function to produce gradients at points using a
global implicit representation. However, the derived gradients usually drift
away from the ground-truth oriented normals due to the lack of local detail
descriptions. Therefore, we introduce Gradient Vector Optimization (GVO) to
learn an angular distance field based on local plane geometry to refine the
coarse gradient vectors. Finally, we formulate our method with a two-phase
pipeline of coarse estimation followed by refinement. Moreover, we integrate
two weighting functions, i.e., anisotropic kernel and inlier score, into the
optimization to improve the robust and detail-preserving performance. Our
method efficiently conducts global gradient approximation while achieving
better accuracy and generalization ability of local feature description. This
leads to a state-of-the-art normal estimator that is robust to noise, outliers
and point density variations. Extensive evaluations show that our method
outperforms previous works in both unoriented and oriented normal estimation on
widely used benchmarks. The source code and pre-trained models are available at
https://github.com/LeoQLi/NGLO.Comment: accepted by SIGGRAPH Asia 202
Neural-Singular-Hessian: Implicit Neural Representation of Unoriented Point Clouds by Enforcing Singular Hessian
Neural implicit representation is a promising approach for reconstructing
surfaces from point clouds. Existing methods combine various regularization
terms, such as the Eikonal and Laplacian energy terms, to enforce the learned
neural function to possess the properties of a Signed Distance Function (SDF).
However, inferring the actual topology and geometry of the underlying surface
from poor-quality unoriented point clouds remains challenging. In accordance
with Differential Geometry, the Hessian of the SDF is singular for points
within the differential thin-shell space surrounding the surface. Our approach
enforces the Hessian of the neural implicit function to have a zero determinant
for points near the surface. This technique aligns the gradients for a
near-surface point and its on-surface projection point, producing a rough but
faithful shape within just a few iterations. By annealing the weight of the
singular-Hessian term, our approach ultimately produces a high-fidelity
reconstruction result. Extensive experimental results demonstrate that our
approach effectively suppresses ghost geometry and recovers details from
unoriented point clouds with better expressiveness than existing fitting-based
methods
Progressive Discrete Domains for Implicit Surface Reconstruction
International audienceMany global implicit surface reconstruction algorithms formulate the problem as a volumetric energy minimization, trading data fitting for geometric regularization. As a result, the output surfaces may be located arbitrarily far away from the input samples. This is amplified when considering i) strong regularization terms, ii) sparsely distributed samples or iii) missing data. This breaks the strong assumption commonly used by popular octree-based and triangulation-based approaches that the output surface should be located near the input samples. As these approaches refine during a pre-process, their cells near the input samples, the implicit solver deals with a domain discretization not fully adapted to the final isosurface.We relax this assumption and propose a progressive coarse-to-fine approach that jointly refines the implicit function and its representation domain, through iterating solver, optimization and refinement steps applied to a 3D Delaunay triangulation. There are several advantages to this approach: the discretized domain is adapted near the isosurface and optimized to improve both the solver conditioning and the quality of the output surface mesh contoured via marching tetrahedra
Dynamic Sculpting and Deformation of Point Set Surfaces
This paper presents a novel paradigm for point set surface editing, which takes advantages of the potential of implicit surfaces, the strength of physics based modeling techniques, and the simplicity of point sampled surfaces. Our point set surface is evaluated as the zero set of the weighted sum of the collection of the scalar trivariate B-spline functions defined over the local domain of each point sample. The implicit representation of the point set surfaces allows users to easily modify the topology of the sculpted objects. The deformation of the surfaces is conducted by dynamically modifying the local reference domains, as well as their scalar control coefficients. We have developed a variety of sculpting toolkits that can dynamically manipulate the implicit point set surface and easily perform CSG boolean operatio