The ball-pivoting algorithm for surface reconstruction

By Fausto Bernardini, Joshua Mittleman, Holly E. Rushmeier, Cláudio T. Silva, Gabriel Taubin. 1999

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Citations in the corpus

Occupancy Networks: Learning 3D Reconstruction in Function Space (2018, background): “However, unlike other representations, this approach requires additional non-trivial post-processing steps [3,6,37,38] to generate the ﬁnal 3D mesh.”
Learning Implicit Fields for Generative Shape Modeling (2018, methodology): “For PC-GAN, we employed the ball-pivoting method [6] to reconstruct the shape surface, while for all the other generative models, we used Marching Cubes.”
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images (2018, methodology): “To compare the quality of mesh model, we convert volumetric and point cloud to mesh using standard approaches [21, 1].”
Point set surfaces (2001, methodology): “There are several efficient triangulation techniques, such as [1, 2, 4, 6, 12].”
Shape As Points: A Differentiable Poisson Solver (2021, background): “The ball pivoting algorithm [5] leverages the continuity property of spherical balls of a given radius.”
Out-of-core simplification of large polygonal models (2000, unknown): “[3, 4, 10].”
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer (2015, background): “[4], considering only 3D points in front of the mesh.”
Neural Unsigned Distance Fields for Implicit Function Learning (2020, methodology): “Since we are able to eﬃciently extract millions of points, naive classical algorithms for meshing [8] (which locally connect the dots) can be used to generate high quality meshes.”
SAL: Sign Agnostic Learning of Shapes From Raw Data (2019, methodology): “Figure 5 shows surface reconstructions based on four raw point clouds provided in [21] with three methods: ball-pivoting [6], variation-implicit reconstruction [21], and SAL based on the L distance, i.”
ShapeFormer: Transformer-based Shape Completion via Sparse Representation (2022, background): “However, point clouds need to be non-trivially post-processed using classical methods [6, 36, 39, 40] to recover surfaces due to their sparse nature.”
Geometric Signal Processing on Polygonal Meshes (2000, methodology): “If we define the matrixK = I W, with I the identity matrix, the Laplacian operator applied to a graph signal x can be written in matrix form as follows ∆x= K x : (3)”
3D Skeletons: A State‐of‐the‐Art Report (2016, methodology): “To obtain separate meshed manifolds, one can cluster such points into manifolds based on their type and distance, and next use proven surface-reconstruction methods [BMR ∗ 99] from manifold point-clouds on each such cluster [KJT14a].”