I am reading the paper : http://mi-lab.org/files/2014/10/FlexSense_web.pdf . I have problems understanding use of ground truth the following :
Main Pipeline Reconstructing the full 3D surface shape from a sparse set of sensor measurements is clearly a challenging task. Each sensor reading from our foil is an amplified voltage measurement, and somehow we need to map these combined values to a real-world reconstruction of the surface. In this section we present two data-driven algorithms that tackle this problem. Both of our methods are first trained using pairs of sensor measurements and ground truth 3D shape measurements of the foil. This pre-processing training phase is what enables our algorithms to infer the shape of the foil from the sparse sensor data at runtime. To collect ground truth measurements of the shape of the foil together with corresponding sensor measurements, we follow the approach illustrated in Figure 5. We print an array of markers on a sheet of paper covering our sensor foil. Then we use a custom-built multi-camera rig (described later) to track the 3D position of the markers with high accuracy. We leverage multiple cameras in order to track as many markers as possible despite occlusions due to the deforming foil and interacting hands.
What exactly does ground truth mean ?