ABSTRACT
We present a low-density point eating algorithm for surface reconstruction from dense scans. First, the density map for each scan is estimated and the boundary densities are down-weighted. Subsequently, the poorly scanned low-density overlapping points are eaten up based on a user-specified threshold. Finally, the overlapping areas are thinned by using the moving least-squares operator and the homogeneous points are weighted averaged. The new algorithm can extract smooth but detailed point set surfaces that are as close as possible to the ground truth. The good performance of the new algorithm is demonstrated by comparison with several advanced surface reconstruction algorithms.
ABSTRACT
Dot-grid images are usually captured for grid strain analysis during sheet metal forming. Due to the strong reflective characteristic of the metallic surfaces, the recorded dot-grid images often have poor quality, low positioning accuracy, and low recognition rate. Therefore, an exposure-fusion-based dot-grid image acquisition and recognition approach is proposed. First, multiple dot-grid images are captured at different exposure levels. Subsequently, the recorded multi-exposure dot-grid images are fused into a new high-quality dot-grid image based on exposure fusion technology. Finally, a dot-grid image recognition procedure is developed to detect the dot-grids in the new dot-grid image. Both synthetic and real dot-grid images were tested to verify the performance of the novel approach. When synthetic dot-grid images were tested, the maximum positioning error was up to 6.044 pixels if they were recognized in the traditional way, whereas the maximum positioning error was reduced to 0.132 pixels if the novel approach was adopted. When real dot-grid images were tested, the lowest recognition rate is only 50.52% if they were recognized in the traditional way. Nevertheless, the recognition rate can reach about 91% if the novel approach was employed. These experimental results show the superiorities of the novel approach.
