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In this paper, the comparison of a novel key-point image descriptors such as DAISY, BRISK, A-KAZE and LATCH with the well-known SIFT and SURF descriptors are tested and compared for the stereo matching algorithm. The main idea of this paper is to present an independent, comparative study and some of the benefits and drawbacks of these most popular image descriptors on stereo images. These descriptors are the primary input for the image correspondence algorithm (stereo matching algorithm). On this assumption, it is possible to estimate depth information from two stereo images. Two sets of experiments are conducted for relative performance evaluations. In the first part of our experiments, the accuracy of stereo matching algorithm using descriptors is demonstrated. The overall time execution is evaluated in the second set of our experiments. The all experiments have been tested on Middlebury stereo-dataset using programming language Python. The experimental result shows that the DAISY descriptor provides better results as A-KAZE or LATCH. On the other hand, DAISY descriptor is slower than SURF. The algorithm based on a DAISY descriptor is effective for matching stereo image pair and these correspondences can be used as input for 3D reconstruction.

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Multiview 3D reconstruction techniques enable digital reconstruction of 3D objects from the real world by fusing different viewpoints of the same object into a single 3D representation. This process is by no means trivial and the acquisition of high quality point cloud representations of dynamic 3D objects is still an open problem. In this paper, an approach for high fidelity 3D point cloud generation using low cost 3D sensing hardware is presented. The proposed approach runs in an efficient low-cost hardware setting based on several Kinect v2 scanners connected to a single PC. It performs autocalibration and runs in real-time exploiting an efficient composition of several filtering methods including Radius Outlier Removal (ROR), Weighted Median filter (WM) and Weighted Inter-Frame Average filtering (WIFA). The performance of the proposed method has been demonstrated through efficient acquisition of dense 3D point clouds of moving objects.