Furthermore, MLS-based correspondences are highly robust to noise. Instead of using a discrete point set for correspondence search, the approach is based on a locally reconstructed continuous moving least squares surface to overcome sampling mismatches in the input shapes. In this paper, we provide a in-depth evaluation of an approach that computes high-quality correspondences for pair-wise closest point-based iterative registration and compare the results with state-of-the-art registration algorithms. An essential problem of registration algorithms is the determination of correspondences between surfaces. Registering partial shapes plays an important role in numerous applications in the fields of robotics, vision, and graphics. Global model represented by an implicit moving least square surface. Method to register and integrate multiple depth frames over time onto a To achieve that goal, in this paper we propose a new Thus, we need a good registrationĪnd integration method that is specifically designed for such low Limitation, the speed of depth camera could be leveraged to combine dataįrom multiple depth frames together. Information into computer vision techniques. Resolution, which poses a real obstacle to incorporating the new 3D

However, a trade-off between the speed and the quality of the results.ĭepth images acquired with current depth cameras are noisy and have low Objects are no longer required to be static for 3D sensing. This speed enables new applications, in that

Information up to 30 frames per second, which is much faster than

With depth camera, one can have access to depth The way humans record the world and interact with 3D virtualĮnvironments. Depth camera is a new technology that has potential to radically change

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