KEYWORDS: Cameras, Detection and tracking algorithms, Imaging systems, 3D modeling, Optical tracking, Databases, Systems modeling, Performance modeling, Optical engineering, RGB color model
We present an approach for real-time camera tracking with depth stream. Existing methods are prone to drift in sceneries without sufficient geometric information. First, we propose a new weight method for an iterative closest point algorithm commonly used in real-time dense mapping and tracking systems. By detecting uncertainty in pose and increasing weight of points that constrain unstable transformations, our system achieves accurate and robust trajectory estimation results. Our pipeline can be fully parallelized with GPU and incorporated into the current real-time depth camera tracking system seamlessly. Second, we compare the state-of-the-art weight algorithms and propose a weight degradation algorithm according to the measurement characteristics of a consumer depth camera. Third, we use Nvidia Kepler Shuffle instructions during warp and block reduction to improve the efficiency of our system. Results on the public TUM RGB-D database benchmark demonstrate that our camera tracking system achieves state-of-the-art results both in accuracy and efficiency.
KEYWORDS: 3D image processing, Reconstruction algorithms, 3D image reconstruction, 3D modeling, Laser imaging, Image processing, Clouds, 3D displays, Imaging systems, 3D acquisition
Research on three-dimensional (3D) surface reconstruction from range slices obtained from range-gated laser imaging system is of significance. 3D surfaces reconstructed based on existing binarization method or centroid method are rough or discontinuous in some circumstances. In this paper we address these problems and develop a 3D surface reconstruction algorithm based on the idea that combining the centroid method with weighted linear interpolation and mean filter. The algorithm consists of three steps. In the first step, interesting regions are extracted from each range slice based on mean filter, and then are merged to derive a single range image. In the second step, the derived range image is denoised and smoothed based on adaptive histogram method, weighted linear interpolation and mean filter method respectively. Finally, nonzero valued pixels in the after processed range image are converted to point cloud according to the range-gated imaging parameters, and then 3D surface meshes are established from the point cloud based on the topological relationship between adjacent pixels in the range image. Experiment is conducted on range slices generated from range-gated laser imaging simulation platform, and the registration result of the reconstructed surface of our method with the original surface of the object shows that the proposed method can reconstruct object surface accurately, so it can be used for the designing of reconstruction and displaying of range-gated laser imaging system, and also can be used for 3D object recognition.
KEYWORDS: 3D modeling, 3D image processing, Target recognition, Object recognition, 3D acquisition, Detection and tracking algorithms, Image processing, 3D imaging standards, Image filtering, Principal component analysis
Spin image has been applied to 3D object recognition system successfully because of its advantages of rotation,
translation and view invariant. However, this method is very time consuming, owning to its high-dimensional
characteristics and its complicated matching procedure. To reduce the recognition time, in this paper we propose a
coarse-to-fine matching strategy for spin images. There are two steps to follow. Firstly, a low dimensional feature is
introduced for a given point. The feature contain two components, its first component is the perpendicular distance from
the centroid of the given point’s neighbor region to the tangential plane of the given point, its second component is the
maximum distance between the projection point of the centroid on the tangential plane and projection points of the
neighbor region on the tangential plane. Secondly when comparing a point from a target with a point from a model, their
low features are matched first, only if they satisfy the low feature constrains, can they be selected as a candidate point
pair and their spin images are further matched by similarity measurement. When all the target points and all the model
points finish above matching process, those candidate point pairs with high spin image similarity are selected as
corresponding point pairs, and the target can be recognized as the model with the most amount of corresponding point
pairs. Experiment based on Stanford 3D models is conducted, and the comparison of experiment results of our method
with the standard spin image shows that the propose method is more efficient while still maintain the standard spin
image’s advantages.
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