4 research outputs found
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
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