Darren Tsai — accepted AI-conference papers

2023

Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection

Darren Tsai, Julie Stephany Berrio, Mao Shan, Eduardo Nebot, Stewart Worrall

ICRA 2023poster

Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are traine…

Cited by 20 Source ↗code ↗Scholar ↗

2022

See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation

Darren Tsai, Julie Stephany Berrio, Mao Shan, Stewart Worrall, Eduardo M. Nebot

RA-L 2022

Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brou

Cited by 18 Source ↗code ↗Scholar ↗