3D models, particularly AI-generated ones, have wit-nessed a recent surge across various industries such as en-tertainment. Hence, there is an alarming need to protect the intellectual property and avoid the misuse of these valuable assets. As a viable solution to address these concerns, we rigorously define the novel task of automated 3D visible wa-termarking in terms of two competing aspects: watermark quality and asset utility. Moreover, we propose a method of embedding visible watermarks that automatically deter-mines the right location, orientation, and number of wa-termarks to be placed on arbitrary 3D assets for high wa-termark quality and asset utility. Our method is based on a novel rigid-body optimization that uses back-propagation to automatically learn transforms for ideal watermark place-ment. In addition, we propose a novel curvature-matching method for fusing the watermark into the 3D model that further improves readability and security. Finally, we provide a detailed experimental analysis on two benchmark 3D datasets validating the superior performance of our approach in comparison to baselines. Code and demo are available here11https://developer.huaweicloud.com/develop/aigallery/notebook/detail?id=15adbaaa-2583-4ec3-804a-61c29f001e03.