activity
20182026
most citedAutomated Evaluation of Semantic Segmentation Robustness for Autonomous Driving

104 citations · 325 across the 28 of their papers we have counts for

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13 papers · 1 filter

cs.CV2023★ 7 cited

MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaptation in 3D Object Detection

Darren Tsai, Julie Stephany Berrio, Mao Shan +2

Deploying 3D detectors in unfamiliar domains has been demonstrated to result in a significant 70-90% drop in detection rate due to variations in lidar, geography, or weather from t…

cs.CV2023

LightFormer: An End-to-End Model for Intersection Right-of-Way Recognition Using Traffic Light Signals and an Attention Mechanism

Zhenxing Ming, Julie Stephany Berrio, Mao Shan +2

For smart vehicles driving through signalised intersections, it is crucial to determine whether the vehicle has right of way given the state of the traffic lights. To address this…

cs.CV2023

MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection

Darren Tsai, Julie Stephany Berrio, Mao Shan +2

We introduce Multi-Source 3D (MS3D), a new self-training pipeline for unsupervised domain adaptation in 3D object detection. Despite the remarkable accuracy of 3D detectors, they o…

cs.CV2022★ 1 cited

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

Darren Tsai, Julie Stephany Berrio, Mao Shan +2

Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors te…

cs.CV2021★ 20 cited

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

Darren Tsai, Julie Stephany Berrio, Mao Shan +2

Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D…

cs.CV2021

Optimising the selection of samples for robust lidar camera calibration

Darren Tsai, Stewart Worrall, Mao Shan +2

We propose a robust calibration pipeline that optimises the selection of calibration samples for the estimation of calibration parameters that fit the entire scene. We minimise use…