6 papers
Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis
Chamuditha Jayanga Galappaththige, Thomas Gottwald, Peter Stehr +4
Recent advances in 3D Gaussian Splatting have enabled impressive photorealistic novel view synthesis. However, to transition from a pure rendering engine to a reliable spatial map…
Domain-Agnostic Feature Modulation for Semi-Supervised Domain Generalization
Venuri Amarasinghe, Kalinga Bandara, Isun Randila +3
Semi-supervised domain generalization (SSDG) leverages a small fraction of labeled data alongside unlabeled data to enhance model generalization. Most of the existing SSDG methods…
From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting
Chamuditha Jayanga Galappaththige, Jason Lai, Timothy Patten +3
Scene change detection methods built on Gaussian splatting universally follow a render-then-compare paradigm: the pre-change scene is rendered into 2D and compared against post-cha…
Changes in Real Time: Online Scene Change Detection with Multi-View Fusion
Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim +3
Online Scene Change Detection (SCD) is an extremely challenging problem that requires an agent to detect relevant changes on the fly while observing the scene from unconstrained vi…
PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage
Thomas Gottwald, Edgar Heinert, Peter Stehr +2
We introduce Primitive-based Representations of Uncertainty (PRIMU), a post-hoc uncertainty estimation (UE) framework for Gaussian Splatting (GS). Reliable UE is essential for depl…
Multi-View Pose-Agnostic Change Localization with Zero Labels
Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim +3
Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and incon…