3,997 research outputs found
Point cloud segmentation using hierarchical tree for architectural models
Recent developments in the 3D scanning technologies have made the generation
of highly accurate 3D point clouds relatively easy but the segmentation of
these point clouds remains a challenging area. A number of techniques have set
precedent of either planar or primitive based segmentation in literature. In
this work, we present a novel and an effective primitive based point cloud
segmentation algorithm. The primary focus, i.e. the main technical contribution
of our method is a hierarchical tree which iteratively divides the point cloud
into segments. This tree uses an exclusive energy function and a 3D
convolutional neural network, HollowNets to classify the segments. We test the
efficacy of our proposed approach using both real and synthetic data obtaining
an accuracy greater than 90% for domes and minarets.Comment: 9 pages. 10 figures. Submitted in EuroGraphics 201
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201
Learning to Construct 3D Building Wireframes from 3D Line Clouds
Line clouds, though under-investigated in the previous work, potentially
encode more compact structural information of buildings than point clouds
extracted from multi-view images. In this work, we propose the first network to
process line clouds for building wireframe abstraction. The network takes a
line cloud as input , i.e., a nonstructural and unordered set of 3D line
segments extracted from multi-view images, and outputs a 3D wireframe of the
underlying building, which consists of a sparse set of 3D junctions connected
by line segments. We observe that a line patch, i.e., a group of neighboring
line segments, encodes sufficient contour information to predict the existence
and even the 3D position of a potential junction, as well as the likelihood of
connectivity between two query junctions. We therefore introduce a two-layer
Line-Patch Transformer to extract junctions and connectivities from sampled
line patches to form a 3D building wireframe model. We also introduce a
synthetic dataset of multi-view images with ground-truth 3D wireframe. We
extensively justify that our reconstructed 3D wireframe models significantly
improve upon multiple baseline building reconstruction methods. The code and
data can be found at https://github.com/Luo1Cheng/LC2WF.Comment: 10 pages, 6 figure
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