A novel watermarking method is presented in which the data embedded into a 3D model is extracted from an arbitrary 2D view by using a perspective projective invariant. The data is embedded into 3D positions of selected interest points on a 3D mesh. Determining the interest point modification vectors for ensuring watermark detection constitutes an important part of the proposed method. Different watermark embedding schemes based on optimization of the watermark function are implemented and evaluated. Another important contribution of the proposed method is selection of the interest points to ensure that they remain detectable after data embedding and rendering. A novel method to identify such repeatable interest points is also presented. Simulations are performed on random 3D point sets as well as realistic 3D models. The results indicate that data embedding in 3D and detection in 2D promises a new direction in watermarking research.