229 research outputs found
Mesh saliency via spectral processing
We propose a novel method for detecting mesh saliency, a perceptuallybased
measure of the importance of a local region on a 3D surface mesh.
Our method incorporates global considerations by making use of spectral
attributes of the mesh, unlike most existing methods which are typically
based on local geometric cues. We first consider the properties of the log-
Laplacian spectrum of the mesh. Those frequencies which show differences
from expected behaviour capture saliency in the frequency domain. Information
about these frequencies is considered in the spatial domain at multiple
spatial scales to localise the salient features and give the final salient
areas. The effectiveness and robustness of our approach are demonstrated
by comparisons to previous approaches on a range of test models. The benefits
of the proposed method are further evaluated in applications such as
mesh simplification, mesh segmentation and scan integration, where we
show how incorporating mesh saliency can provide improved results
Event-Based Obstacle Detection with Commercial Lidar
Computerized obstacle detection for moving vehicles is becoming more important as vehicle manufacturers make their systems more autonomous and safe. However, obstacle detection must operate quickly in dynamic environments such as driving at highway speeds. A unique obstacle detection system using 3D changes in the environment is proposed. Furthermore, these 3D changes are shown to contain sufficient information for avoiding obstacles. To make the system easy to integrate onto a vehicle, additional processing is implemented to remove unnecessary dependencies. This system provides a method for obstacle detection that breaks away from typical systems to be more efficient
Registration of 3D Point Clouds and Meshes: A Survey From Rigid to Non-Rigid
Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization. Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends
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