TY - GEN
T1 - Towards Dual Transparent Liquid Level Estimation in Biomedical Lab
T2 - 18th European Conference on Computer Vision, ECCV 2024
AU - Wang, Xiayu
AU - Ma, Ke
AU - Zhong, Ruiyun
AU - Wang, Xinggang
AU - Fang, Yi
AU - Xiao, Yang
AU - Xia, Tian
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - “Dual Transparent Liquid” refers to a liquid and its container, both being transparent. Accurately estimating the levels of such a liquid from arbitrary viewpoints is fundamental and crucial, especially in AI-guided autonomous biomedical laboratories for tasks like liquid dispensing, aspiration, and mixing. However, current methods for estimating liquid level focus on scenarios of a single instance captured from a fixed view. We propose a new dual transparent liquid level estimation paradigm, including a dataset, methods, and practices. The dual transparent liquid dataset, named DTLD, comprises 27,458 images with four object instances captured from multiple views across three biomedical lab scenes. Based on DTLD, we propose an end-to-end learning method for detecting the liquid contact line, followed by an approach to estimate the liquid level. To enhance contact line detection, a color rectification module is proposed to stabilize the color distribution at the local region of the air-liquid interface. Additionally, our method surpasses the current best approach, reducing the mean absolute percentage error by a percentage of 43.4. The dataset and code are available at https://github.com/dualtransparency/TCLD.
AB - “Dual Transparent Liquid” refers to a liquid and its container, both being transparent. Accurately estimating the levels of such a liquid from arbitrary viewpoints is fundamental and crucial, especially in AI-guided autonomous biomedical laboratories for tasks like liquid dispensing, aspiration, and mixing. However, current methods for estimating liquid level focus on scenarios of a single instance captured from a fixed view. We propose a new dual transparent liquid level estimation paradigm, including a dataset, methods, and practices. The dual transparent liquid dataset, named DTLD, comprises 27,458 images with four object instances captured from multiple views across three biomedical lab scenes. Based on DTLD, we propose an end-to-end learning method for detecting the liquid contact line, followed by an approach to estimate the liquid level. To enhance contact line detection, a color rectification module is proposed to stabilize the color distribution at the local region of the air-liquid interface. Additionally, our method surpasses the current best approach, reducing the mean absolute percentage error by a percentage of 43.4. The dataset and code are available at https://github.com/dualtransparency/TCLD.
KW - Autonomous biomedical laboratory
KW - Contact line detection
KW - Dual transparent liquid
KW - Liquid level estimation
UR - http://www.scopus.com/inward/record.url?scp=85210477646&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85210477646&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73650-6_12
DO - 10.1007/978-3-031-73650-6_12
M3 - Conference contribution
AN - SCOPUS:85210477646
SN - 9783031736490
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 198
EP - 214
BT - Computer Vision – ECCV 2024 - 18th European Conference, Proceedings
A2 - Leonardis, Aleš
A2 - Ricci, Elisa
A2 - Roth, Stefan
A2 - Russakovsky, Olga
A2 - Sattler, Torsten
A2 - Varol, Gül
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 29 September 2024 through 4 October 2024
ER -