Forest is one of the most challenging environments to be recorded in a three-dimensional (3D) digitized geometrical representation, because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions. Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors. In practice, the ideal short-baseline observations, i.e., the dense collection mode, is rarely feasible, considering the low accessibility in forest environments and the commonly limited labor and time resources. The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations, are therefore more preferable and commonly applied. Nevertheless, the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets. Until now, a robust automated registration solution that is independent of special hardware requirements has still been missing. That is, the registration accuracy is still far from the required level, and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration. This paper proposes a discrete overlap search (DOS) method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds. The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level. An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels. The performance of the proposed method was evaluated using various accuracy criteria, as well as based on data acquired from different hardware, platforms, viewing perspectives, and at different points of time. The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions. In the terrestrial-aerial registration, data sets were collected from different sensors and at different points of time with scene changes, and a registration accuracy at the raw data geometric accuracy level was achieved. These results represent the highest automated registration accuracy and the strictest evaluation so far. The proposed method is applicable in multiple scenarios, such as 1) the global positioning of individual under-canopy observations, which is one of the main challenges in applying terrestrial observations lacking a global context, 2) the fusion of point clouds acquired from terrestrial and aerial perspectives, which is required in order to achieve a complete forest observation, 3) mobile mapping using a new stop-and-go approach, which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach. Furthermore, this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter- and object-level error estimates; it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.