18 citations · 30 across the 9 of their papers we have counts for
9 papers
SABER-6D: Shape Representation Based Implicit Object Pose Estimation
Shishir Reddy Vutukur, Mengkejiergeli Ba, Benjamin Busam +2
In this paper, we propose a novel encoder-decoder architecture, named SABER, to learn the 6D pose of the object in the embedding space by learning shape representation at a given p…
Joint Sampling and Optimisation for Inverse Rendering
Martin Balint, Karol Myszkowski, Hans-Peter Seidel +1
When dealing with difficult inverse problems such as inverse rendering, using Monte Carlo estimated gradients to optimise parameters can slow down convergence due to variance. Aver…
Patternshop: Editing Point Patterns by Image Manipulation
Xingchang Huang, Tobias Ritschel, Hans-Peter Seidel +2
Point patterns are characterized by their density and correlation. While spatial variation of density is well-understood, analysis and synthesis of spatially-varying correlation is…
Regression-based Monte Carlo Integration
Corentin Salaün, Adrien Gruson, Binh-Son Hua +2
Monte Carlo integration is typically interpreted as an estimator of the expected value using stochastic samples. There exists an alternative interpretation in calculus where Monte…
Scalable multi-class sampling via filtered sliced optimal transport
Corentin Salaün, Iliyan Georgiev, Hans-Peter Seidel +1
We propose a multi-class point optimization formulation based on continuous Wasserstein barycenters. Our formulation is designed to handle hundreds to thousands of optimization obj…
Neural Relightable Participating Media Rendering
Quan Zheng, Gurprit Singh, Hans-Peter Seidel
Learning neural radiance fields of a scene has recently allowed realistic novel view synthesis of the scene, but they are limited to synthesize images under the original fixed ligh…
Blue Noise Plots
Christian van Onzenoodt, Gurprit Singh, Timo Ropinski +1
We propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often one-dimensional strip plots are used to depict such data, one of…
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry
Yifan Xu, Tianqi Fan, Yi Yuan +1
Deep implicit field regression methods are effective for 3D reconstruction from single-view images. However, the impact of different sampling patterns on the reconstruction quality…
End-to-end Sampling Patterns
Thomas Leimkühler, Gurprit Singh, Karol Myszkowski +2
Sample patterns have many uses in Computer Graphics, ranging from procedural object placement over Monte Carlo image synthesis to non-photorealistic depiction. Their properties suc…