Demo Data from SIGIR 2026 / reviewers in the wild / expert
Dounian Ma
dblp:431/5089
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 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
1 paper |
Rendering · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering › gaussian splatting
3d gaussian splatting |
1.0 | 1 | 2026 | PATexGS: Perceptual-Adaptive Texture Scheduling for Visual Coherence in Textured Gaussian Splatting · AAAI 2026 |
Rendering
neural rendering |
1.0 | 1 | 2026 | PATexGS: Perceptual-Adaptive Texture Scheduling for Visual Coherence in Textured Gaussian Splatting · AAAI 2026 |
Rendering
texture mapping |
1.0 | 1 | 2026 | PATexGS: Perceptual-Adaptive Texture Scheduling for Visual Coherence in Textured Gaussian Splatting · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
mipmap · 1.0entropy-guided allocation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | PATexGS: Perceptual-Adaptive Texture Scheduling for Visual Coherence in Textured Gaussian Splattingabstract3D Gaussian Splatting (3DGS) has emerged as a mainstream solution for real-time rendering and high-fidelity novel view synthesis. Building on this foundation, methods based on Textured Gaussians further improve the expression ability by incorporating explicit texture mapping into Gaussians. However, their reliance on fixed texture resolution often results in noticeable visual incoherence, triggering artifacts such as aliasing or inconsistent sharpness under different viewpoints. To address these issues, we propose PATexGS, a perceptual-adaptive texture scheduling framework designed to improve visual coherence for Textured Gaussians. Specifically, we introduce an entropy-guided texture allocation strategy that dynamically adjusts texture resolution based on each Gaussian’s spatial gradient and rendering contribution, constantly preserving details while being memory efficiency. Furthermore, we incorporate a mipmap-inspired hierarchical scheduling mechanism that adaptively schedule texture levels according to view-dependent projection scale, effectively suppressing aliasing and further enhancing perceptual consistency. Extensive experiments on diverse real-world scenes demonstrate that PATexGS significantly improves visual coherence while maintaining high rendering quality, outperforming existing TexturedGS variants in both perceptual fidelity and storage efficiency. Yuesong Wang 0001, Dounian Ma |
AAAI | 2 |