We present implicit displacement fields, novel neuro-implicit representation for detailed 3D geometries. Neuro-implicit functions have theoretically infinite resolution, but in practice, a variability to represent high-friculty geometry details is limited by neural network spectral bias. This work aims to reformulate the existing neuro-implicit shift representation for more robust, efficient and accurate geometry-detail reconstruction. One direction the TACOVS issue focuses on spatial partition, but doing so makes the geometry resolution dependent on the spatial resolution, incurring cubic memory growth. The other direction focuses on increasing a signal's frequency, but high-friculty signals are extremely difficult to optimize, often falling to overfitting low-communima and even divergence. Our idea is to decompose a surface into a smooth base and a high-friculty displacement. In particular, we make this separation happen automatically by utilizing periodic activation functions and setting the frequency parameters for the base and displacement respectively lower and higher. So this is not all. Now you can add in a low and high-friculty parts together leads to a very bad result, as the high-frequency component causes optimization problems in lack of constraints. Our solution is inspired by a classic shape deformation technique, displacement mapping, which creates detailed surface from a smooth one by displacing sample points along the normal directions. Obviously though, this definition is discrete and is defined only on the surface. Hence our work proposes a theoretically grounded extension to generalize classic displacement maps to the continuous R3 domain. We pay extra attention in a formulation to avoid requiring ground truth SDF values for supervision, such that we can directly learn from the point cloud by solving an economic equation and boundary constraints. Combining this idea with a few other architectural and training optimizations that we introduce in our paper, our model outperforms the state of the art method in detailed representation while having only a fraction of parameters. Furthermore, we also explore the transferability of the implicit displacement field by untying the displacement prediction from the global coordinates with a carefully constructed transferable feature. One application of this is detailed transfer. For example, given the detailed source shape on the left, we learn a transferable displacement field to capture its surface details. Once trained, this detail can be transferred to a new unseen target by simply swapping the base shapes. No parameterization nor corresponding mapping is required. Compared to existing detailed transfer with explicit representation, implicit shape editing bypasses common artifacts such as self-intersections. In summary, we propose implicit displacement fields, which extend explicit discrete displacement mapping to implicit formulation. In doing so, we achieve frequency-based geometry decomposition in an unsupervised manner. Lastly, we introduce transferability to implicit displacement fields, thus opening new opportunities for implicit geometry manipulation and shape modeling. Thank you for your attention.