7 research outputs found
T-spline based unifying registration procedure for free-form surface workpieces in intelligent CMM
With the development of the modern manufacturing industry, the free-form surface is widely used in various fields, and the automatic detection of a free-form surface is an important function of future intelligent three-coordinate measuring machines (CMMs). To improve the intelligence of CMMs, a new visual system is designed based on the characteristics of CMMs. A unified model of the free-form surface is proposed based on T-splines. A discretization method of the T-spline surface formula model is proposed. Under this discretization, the position and orientation of the workpiece would be recognized by point cloud registration. A high accuracy evaluation method is proposed between the measured point cloud and the T-spline surface formula. The experimental results demonstrate that the proposed method has the potential to realize the automatic detection of different free-form surfaces and improve the intelligence of CMMs
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
This paper addresses the problem of generating uniform dense point clouds to
describe the underlying geometric structures from given sparse point clouds.
Due to the irregular and unordered nature, point cloud densification as a
generative task is challenging. To tackle the challenge, we propose a novel
deep neural network based method, called PUGeo-Net, that learns a
linear transformation matrix for each input point. Matrix
approximates the augmented Jacobian matrix of a local parameterization and
builds a one-to-one correspondence between the 2D parametric domain and the 3D
tangent plane so that we can lift the adaptively distributed 2D samples (which
are also learned from data) to 3D space. After that, we project the samples to
the curved surface by computing a displacement along the normal of the tangent
plane. PUGeo-Net is fundamentally different from the existing deep learning
methods that are largely motivated by the image super-resolution techniques and
generate new points in the abstract feature space. Thanks to its
geometry-centric nature, PUGeo-Net works well for both CAD models with sharp
features and scanned models with rich geometric details. Moreover, PUGeo-Net
can compute the normal for the original and generated points, which is highly
desired by the surface reconstruction algorithms. Computational results show
that PUGeo-Net, the first neural network that can jointly generate vertex
coordinates and normals, consistently outperforms the state-of-the-art in terms
of accuracy and efficiency for upsampling factor .Comment: 17 pages, 10 figure