Christina Tsalicoglou — accepted AI-conference papers

2025

LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering

Jonas Kulhanek, Marie-Julie Rakotosaona, Fabian Manhardt, Christina Tsalicoglou, Michael Niemeyer, Torsten Sattler +2

NeurIPS 2025spotlight

In this work, we present a novel level-of-detail (LOD) method for 3D Gaussian Splatting that enables real-time rendering of large-scale scenes on memory-constrained devices. Our approach introduces a hierarchical LOD representation that iteratively selects optimal subsets of Gaussians based on camer…

Cited by 0 Source ↗Scholar ↗

2025

Learning Neural Exposure Fields for View Synthesis

Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona, Michael Oechsle, Christina Tsalicoglou, Keisuke Tateno +2

NeurIPS 2025poster

Recent advances in neural scene representations have led to unprecedented quality in 3D reconstruction and view synthesis. Despite achieving high-quality results for common benchmarks with curated data, outputs often degrade for data that contain per image variations such as strong exposure changes,…

Cited by 0 Source ↗Scholar ↗

2022

Using 3D Topological Connectivity for Ghost Particle Reduction in Flow Reconstruction

Christina Tsalicoglou, Thomas Rösgen

CVPR 2022poster

Volumetric flow velocimetry for experimental fluid dynamics relies primarily on the 3D reconstruction of point objects, which are the detected positions of tracer particles identified in images obtained by a multi-camera setup. By assuming that the particles accurately follow the observed flow, thei…

Cited by 1 PDF ↗Scholar ↗