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Chamuditha Jayanga Galappaththige

5 accepted papers

2026

Changes in Real Time: Online Scene Change Detection with Multi-View Fusion

Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau, Niko Suenderhauf, Dimity Miller
CVPR 2026

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 viewpoints. Existing online SCD methods are significantly less accurate than offline approaches. We present the first onli

Cited by 0Source ↗code ↗Scholar ↗
2026

PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage

Thomas Gottwald, Edgar Heinert, Peter Stehr, Chamuditha Jayanga Galappaththige, Matthias Rottmann
CVPR 2026

We introduce Primitive-based Representations of Uncertainty (PRIMU), a post-hoc uncertainty estimation (UE) framework for Gaussian Splatting (GS).Reliable UE is essential for deploying GS in safety-critical domains such as robotics and medicine.Existing approaches typically estimate Gaussian-primiti

Cited by 0Source ↗Scholar ↗
2025

Multi-View Pose-Agnostic Change Localization with Zero Labels

Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau, Niko Sunderhauf, Dimity Miller
CVPR 2025poster

Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information fro…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Adversarial Text to Continuous Image Generation

Kilichbek Haydarov, Aashiq Muhamed, Xiaoqian Shen, Jovana Lazarevic, Ivan Skorokhodov, Chamuditha Jayanga Galappaththige +1
CVPR 2024poster

Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper we approach the text-to-image task from a different perspective where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based…

Cited by 3Source ↗PDF ↗Scholar ↗
2024

Towards Generalizing to Unseen Domains with Few Labels

Chamuditha Jayanga Galappaththige, Sanoojan Baliah, Malitha Gunawardhana, Muhammad Haris Khan
CVPR 2024poster

We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generaliz…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗