“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 paradigm for dual transparent liquid level estimation, 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 will be available at https://github.com/dualtransparency/TCLD.