Information entropy-based viewpoint planning for 3-D object reconstruction

Abstract

In this paper, we present an information entropybased viewpoint-planning approach for reconstruction of freeform surfaces of three-dimensional objects. To achieve the reconstruction, the object is first sliced into a series of cross section curves, with each curve to be reconstructed by a closed B-spline curve. In the framework of Bayesian statistics, we propose an improved Bayesian information criterion (BIC) for determining the B-spline model complexity. Then, we analyze the uncertainty of the model using entropy as the measurement. Based on this analysis, we predict the information gain for each cross section curve for the next measurement. After predicting the information gain of each curve, we obtain the information change for all the B-spline models. This information gain is then mapped into the view space. The viewpoint that contains maximal information gain about the object is selected as the next best view. Experimental results show successful implementation of our view planning method for digitization and reconstruction of freeform objects.

Determining next best view based on occlusion information in a single depth image of visual object

“…Compared with the method in the study by Li and Liu 12 for the visual objects Mole, Rocker, Bunny, and Dragon, because the occlusion is obvious and the external surface of occluded region is larger, both the number of new added points and the new added rate are significantly better than those of the method in the study by Li and Liu. 12 For the visual object Duck with unobvious occlusion, although its number of new added points and the new added rate are relatively lower than those of visual objects Mole, Rocker, Bunny, and Dragon with obvious occlusion, the performance of proposed method is still clearly superior to that of the method in the study by Li and Liu. 12 In general, because the method in the study by Li and Liu 12 focuses on observing the adjoining unknown region of the largest The depth image in current view…”

“…[11] The depth image in next best view with method in Ref. [12] The depth image in next best view with proposed method Figure 6. The depth image of visual object in next best view for different methods.…”

“…[1][2][3][4][5] Nowadays, there are two main types of image information used to determine next best view, intensity information, [6][7][8] and depth information. [9][10][11][12][13][14][15] The methods based on intensity information are relatively fewer than the methods based on depth information. Compared with 2D intensity image, it is easier to obtain the 3D information of scene from 2.5D depth image; therefore, the existing next best view methods are usually achieved using depth image.…”

On the use of unmanned aerial vehicles for autonomous object modeling

“…In [5] the authors propose an information entropy-based approach to the VPP. With a metric that measures the information gain for a possible next view point of a camera, they are able to pick the next best view out of the set containing all possible next view points.…”

3D Sensor planning framework for leaf probing

“…A typical approach is to make assumptions about the nature of the already captured surfaces. For instance, adjusting B-Splines to sections of the 3D object [12], or modeling the surface as a combination of a trend and some disturbances [13].…”