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.
ABSTRACT
In literature, there are many reports about how to build high-precision, high-speed, and/or flexibly structured light range scanners. Whereas, there are few papers reported about how to implement the scanning to reach the full potential of the scanners when digitizing various objects. In this paper, some scanning strategies adopted by the structured light range scanner XJTUOM are introduced. In order to build high-quality three-dimensional (3D) models for various applications, such as 3D inspection, if necessary, the object surface is prepared in advance by an application of coating spray. Then, a precise 3D coordinate control network (CNN) is established to control the overall measurement accuracy. When the sensor is adjusted to face the object, a visual measuring volume, which can guide the scanning, is automatically aligned to the established CNN. Meanwhile, to maintain the local region scanning accuracy, simple rules are developed to check the low-quality regions in each scanning. Finally, the advantages and limitations of these scanning strategies are discussed in detail. We hope our work will be helpful in order for others to make their own scanning plans with similar optical devices at hand.
ABSTRACT
We present a trilateral scan integration method to extract a desirable single-layer, smooth, detailed point set surface. A density adaptive overlapping areas detection algorithm is developed to detect overlapping points in raw scans. Subsequently, a variant trilateral filter is designed to shift the overlapping points to a single layer surface as well as reduce noises and outliers. Finally, a mean-shift clustering scheme is employed to gather corresponding points iteratively, and the local maxima model replaces members in the stable cluster, thereby removing redundancies. Experimental results demonstrate the superiority of the new method.
