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Dorian Tsai

5 accepted papers

2026

Automated Coral Spawn Monitoring for Reef Restoration: The Coral Spawn and Larvae Imaging Camera System (CSLICS)

Dorian Tsai, Christopher A. Brunner, Riki Lamont, F. Mikaela Nordborg, Andrea Severati, Java Terry +4
ICRA 2026poster

Coral aquaculture for reef restoration requires accurate and continuous spawn counting for resource distribution and larval health monitoring, but current methods are labor-intensive and represent a critical bottleneck in the coral production pipeline. We propose the Coral Spawn and Larvae Imaging C…

Cited by 0Source ↗PDF ↗Scholar ↗
2021

Refractive Light-Field Features for Curved Transparent Objects in Structure From Motion

Dorian Tsai, Peter Corke, Thierry Peynot, Donald G. Dansereau
RA-L 2021

Curved refractive objects are common in the human environment, and have a complex visual appearance that can cause robotic vision algorithms to fail. Light-field cameras allow us to address this challenge by capturing the view-dependent appearance of such objects in a single exposure. We propose a n

Cited by 5Source ↗Scholar ↗
2019

3D Move to See: Multi-perspective visual servoing towards the next best view within unstructured and occluded environments

Chris Lehnert, Dorian Tsai, Anders Eriksson, Chris McCool
IROS 2019poster

In this paper we present a novel approach termed 3D Move to See (3DMTS) which is based on the principle of finding the next best view using a 3D camera array and a robotic manipulator to obtain multiple samples of the scene from different perspectives. Distinct from traditional visual servoing and n…

Cited by 47Source ↗Scholar ↗
2019

Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion

Dorian Tsai, Donald G. Dansereau, Thierry Peynot, Peter Corke
RA-L 2019

To be effective, robots will need to reliably operate in scenes with refractive objects in a variety of applications; however, refractive objects can cause many robotic vision algorithms, such as structure from motion, to become unreliable or even fail. We propose a novel method to distinguish betwe

Cited by 15Source ↗Scholar ↗