Lloyd Windrim — accepted AI-conference 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

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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…

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2019

Forest Tree Detection and Segmentation using High Resolution Airborne LiDAR

Lloyd Windrim, Mitch Bryson

IROS 2019poster

This paper presents an autonomous approach to tree detection and segmentation from high resolution airborne LiDAR pointclouds, such as those collected from a UAV, that utilises region-based CNN and 3D-CNN deep learning algorithms. Trees are first detected in 2D before individual trees are further ch…

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