Publications (6)
Addressing Bias in Visualization Recommenders by Identifying Trends in Training Data: Improving VizML Through a Statistical Analysis of the Plotly Community Feed
Allen Tu, Priyanka Mehta, Alexander Wu +2
Machine learning is a promising approach to visualization recommendation due to its high scalability and representational power. Researchers can create a neural network to predict…
PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting
Alex Hanson, Allen Tu, Vasu Singla +3
Recent advances in novel view synthesis have enabled real-time rendering speeds with high reconstruction accuracy. 3D Gaussian Splatting (3D-GS), a foundational point-based paramet…
SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping
Allen Tu, Haiyang Ying, Alex Hanson +3
Dynamic extensions of 3D Gaussian Splatting (3DGS) achieve high-quality reconstructions through neural motion fields, but per-Gaussian neural inference makes these models computati…
SplatSuRe: Selective Super-Resolution for Multi-view Consistent 3D Gaussian Splatting
Pranav Asthana, Alex Hanson, Allen Tu +3
3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis, motivating interest in generating higher-resolution renders than those available during training. A natural…
TransFIRA: Transfer Learning for Face Image Recognizability Assessment
Allen Tu, Kartik Narayan, Joshua Gleason +4
Face recognition in unconstrained environments such as surveillance, video, and web imagery must contend with extreme variation in pose, blur, illumination, and occlusion, where co…
Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives
Alex Hanson, Allen Tu, Geng Lin +3
3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differe…