The commercial 3-D laser scanners provide high quality reconstructions of different surfaces. However if the reconstructed surfaces are made of diffusing materials and/or exhibit self occluding properties then errors are guaranteed in the output reconstruction. In this paper, we try to rectify this situation by fusing the 3-D reconstructions generated by a 3-D laser scanner and other 3-D reconstruction techniques that may not exhibit similar reconstruction errors. We propose a fusion technique that employees a 3-D alignment criterion and a local quality assessment technique as well. The fusion decision in the proposed technique is guided by the extracted contours from the input images of the object under-reconstruction. The approach can be applied to different 3-D reconstructions from different sensors. The approach exhibits promising results on the fusion of the space carving technique and the 3-D reconstruction by a 3-D laser scanner.

The performance evaluation of 3D reconstruction techniques is not a simple problem to solve. This is not only due to the increased dimensionality of the problem but also due to the lack of standardized and widely accepted testing methodologies. This paper presents a unified framework for the performance evaluation of different 3D reconstruction techniques. This framework includes a general problem formalization, different measuring criteria, and a classification method as a first step in standardizing the evaluation process. Performance characterization of two standard 3D reconstruction techniques, stereo and space carving, is also presented. The evaluation is performed on the same data set using an image reprojection testing methodology to reduce the dimensionality of the evaluation domain. Also, different measuring strategies are presented and applied to the stereo and space carving techniques. These measuring strategies have shown consistent results in quantifying the performance of these techniques. Additional experiments are performed on the space carving technique to study the effect of the number of input images and the camera pose on its performance.