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The advancements in vision-based tactile sensors have boosted the aptitude ofrobots to perform contact-rich manipulation, particularly when precisepositioning and contact state of the manipulated objects are crucial forsuccessful execution. In this work, we present 9DTact, a straightforward yetversatile tactile sensor that offers 3D shape reconstruction and 6D forceestimation capabilities. Conceptually, 9DTact is designed to be highly compact,robust, and adaptable to various robotic platforms. Moreover, it is low-costand easy-to-fabricate, requiring minimal assembly skills. Functionally, 9DTactbuilds upon the optical principles of DTact and is optimized to achieve 3Dshape reconstruction with enhanced accuracy and efficiency. Remarkably, weleverage the optical and deformable properties of the translucent gel so that9DTact can perform 6D force estimation without the participation of auxiliarymarkers or patterns on the gel surface. More specifically, we collect a datasetconsisting of approximately 100,000 image-force pairs from 175 complex objectsand train a neural network to regress the 6D force, which can generalize tounseen objects. To promote the development and applications of vision-basedtactile sensors, we open-source both the hardware and software of 9DTact, alongwith a comprehensive video tutorial, all of which are available athttps://linchangyi1.github.io/9DTact.

9DTact: A Compact Vision-Based Tactile Sensor for Accurate 3D Shape Reconstruction and Generalizable 6D Force Estimation