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Felice Serena

dblp:242/8797 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Geometric modeling and processing · 54% Rendering · 46%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering
differentiable rendering
0.412019
Differentiable surface splatting for point-based geometry processing · ACM Trans. Graph. 2019
Rendering
point-based rendering
0.412019
Differentiable surface splatting for point-based geometry processing · ACM Trans. Graph. 2019
Geometric modeling and processing › point cloud processing
point cloud denoising
0.412019
Differentiable surface splatting for point-based geometry processing · ACM Trans. Graph. 2019
Geometric modeling and processing
point cloud processing
0.412019
Differentiable surface splatting for point-based geometry processing · ACM Trans. Graph. 2019
Geometric modeling and processing › shape modeling
geometry synthesis
0.112019
Differentiable surface splatting for point-based geometry processing · ACM Trans. Graph. 2019

Methods — techniques the papers use, named apart from their topics

surface splatting · 0.4differentiable rendering · 0.4
YearPublicationVenuePosition
2019 Differentiable surface splatting for point-based geometry processing
abstract
We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds. Gradients for point locations and normals are carefully designed to handle discontinuities of the rendering function. Regularization terms are introduced to ensure uniform distribution of the points on the underlying surface. We demonstrate applications of DSS to inverse rendering for geometry synthesis and denoising, where large scale topological changes, as well as small scale detail modifications, are accurately and robustly handled without requiring explicit connectivity, outperforming state-of-the-art techniques. The data and code are at https://github.com/yifita/DSS.
Wang Yifan 0001, Felice Serena, A. Cengiz Öztireli, Olga Sorkine-Hornung
ACM Trans. Graph.2