The motion planning problem consists of finding a valid path between a start and a goal configuration for a movable object (the robot). Approximate methods for motion planning have been studied to overcome the intractability of the problem. Several randomized techniques produce a data structure (usually a graph or roadmap) that represents the connectivity of the space of valid robot configurations (free C-space). Ideally, a roadmap should have connected components that represent the connectivity of the free C-space (C-free) which implies it needs samples (roadmap nodes) in all distinct regions of C-free. While randomized techniques have difficulty in identifying and sampling some areas of C-free, sometimes those regions are readily apparent to human users. We propose to use an interactive 3D visualization tool that allows users to easily add nodes to a roadmap in difficult regions visually detected by the user. These nodes can be used as seed configurations that automatic planners can use to add more nodes around, to facilitate connection of existing roadmap components, or to identify areas to explore.