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Randall Rauwendaal

dblp:148/6554 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0004-6753-9390ORCID · reported

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

Artificial intelligence and machine learning · 2 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

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
2 papers
Rendering · 82% Image and video coding · 18%

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

TopicWeightPapersLastEvidence papers
Rendering
differentiable rendering
0.912025
Sort-free Gaussian Splatting via Weighted Sum Rendering · ICLR 2025
Rendering
gaussian splatting
0.912025
Sort-free Gaussian Splatting via Weighted Sum Rendering · ICLR 2025
Rendering
real-time rendering
0.912025
Sort-free Gaussian Splatting via Weighted Sum Rendering · ICLR 2025
Rendering
neural rendering
0.812024
Neural Graphics Texture Compression Supporting Random Access · ECCV (37) 2024
Image and video coding
texture compression
0.812024
Neural Graphics Texture Compression Supporting Random Access · ECCV (37) 2024

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

weighted sum rendering · 0.9gaussian splatting · 0.9random access · 0.8neural graphics primitives · 0.8
YearPublicationVenuePosition
2025 Sort-free Gaussian Splatting via Weighted Sum Rendering
abstract
Recently, 3D Gaussian Splatting (3DGS) has emerged as a significant advancement in 3D scene reconstruction, attracting considerable attention due to its ability to recover high-fidelity details while maintaining low complexity. Despite the promising results achieved by 3DGS, its rendering performance is constrained by its dependence on costly non-commutative alpha-blending operations. These operations mandate complex view dependent sorting operations that introduce computational overhead, especially on the resource-constrained platforms such as mobile phones. In this paper, we propose Weighted Sum Rendering, which approximates alpha blending with weighted sums, thereby removing the need for sorting. This simplifies implementation, delivers superior performance, and eliminates the ``popping'' artifacts caused by sorting. Experimental results show that optimizing a generalized Gaussian splatting formulation to the new differentiable rendering yields competitive image quality. The method was implemented and tested in a mobile device GPU, achieving on average $1.23\times$ faster rendering.
Qiqi Hou, Randall Rauwendaal, Hoang Le, Farzad Farhadzadeh, Fatih Porikli, Alex Bourd, Amir Said
ICLR2
2024 Neural Graphics Texture Compression Supporting Random Access
Farzad Farhadzadeh, Qiqi Hou, Hoang Le, Amir Said, Randall Rauwendaal, Alex Bourd, Fatih Porikli
ECCV (37)5