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49 followers · 4 following
  • Greenroom Robotics
  • Sydney, Australia
  • LinkedIn in/darrenjkt

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Achievement: Pair ExtraordinaireAchievement: YOLOAchievement: Starstruckx2Achievement: Pull Sharkx2Achievement: Quickdraw

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darrenjkt/README.md

Hey, I'm Darren 👋

I am a computer vision and robotics engineer currently working on making boats autonomous on Australian waters through marine vessel and mammal detection 🚢 🐳 🌊

My PhD focused on 3D perception in autonomous vehicles with a focus on unsupervised domain adaptation to allow detectors to generalize across a variety of lidar types and environments without needing human-annotated labels for each new domain.

Publications 📖

  • (T-IV 2024) MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object Detection [paper][code]
  • (ITSC 2023) MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection [paper][code]
  • (ICRA 2023) Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection [paper][code]
  • (RA-L 2022) See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation [paper][code]
  • (ITSC 2021) Optimising the selection of samples for robust lidar camera calibration [paper][code]

Pinned Loading

  1. MS3D MS3D Public

    Auto-labeling of point cloud sequences for 3D object detection using an ensemble of experts and temporal refinement

    Python 161 18

  2. acfr/cam_lidar_calibration acfr/cam_lidar_calibration Public

    (ITSC 2021) Optimising the selection of samples for robust lidar camera calibration. This package estimates the calibration parameters from camera to lidar frame.

    C++ 457 107

  3. SEE-MTDA SEE-MTDA Public

    (RA-L 2022) See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation.

    Python 48 8

  4. SEE-VCN SEE-VCN Public

    (ICRA 2023) Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection

    Python 20 5