Point-Based Graphics

By Markus H. Gross, Hanspeter Pfister. 2007

311 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

View Paper: https://www.semanticscholar.org/paper/7a1790e70ed2c3881c8af751425ec32dbaac4f0f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

Mip-Splatting: Alias-Free 3D Gaussian Splatting (2023, background): “Primitive-based Differentiable Rendering: Primitive-based rendering techniques, which rasterize geometric primitives onto the image plane, have been explored extensively due to their efficiency [13, 14, 38, 44, 59, 60].”
P2P-NET: Bidirectional Point Displacement Net for Shape Transform (background): “Not for redistribution. years [Gross and Pfister 2007].”
Real-time 3D reconstruction at scale using voxel hashing (2013, methodology): “Point-based methods (e.g., [Rusinkiewicz et al. 2002; Weise et al. 2009]) use simple unstructured representations that closely map to range and depth sensor input, but lack the ability to directly reconstruct connected surfaces.”
Multi-View Stereo: A Tutorial (2015, background): “Note that a point cloud reconstruction is visualized by a point-based rendering technique [160, 83], which may exhibit an appearance of a complete texture-mapped mesh model, but they are just independent 3D points with colors.”
Edge-aware point set resampling (2013, methodology): “The use of points as a modeling and rendering primitive has also been studied extensively [Gross and Pfister 2007].”
Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering (2023, background): “To alleviate the discontinuity issue, differentiable point-based rendering has been extensively studied to model objects geometry [16, 20, 27, 48, 55].”
Jump (2016, methodology): “We do not use elaborate splat kernels as is typical in point splatting [Gross and Pfister 2007].”
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (2019, methodology): “Denoising and outlier removal also arise prominently, and have therefore been considered in the context of surface ﬁtting to noisy point clouds, including the widely-used Moving Least Squares (MLS) approach and its robust variants [ABCO ∗ 03,MVdF03,FCOS05,ÖGG09,GP11].”