386 research outputs found
Robust Kernel-based Feature Representation for 3D Point Cloud Analysis via Circular Graph Convolutional Network
Feature descriptors of point clouds are used in several applications, such as
registration and part segmentation of 3D point clouds. Learning discriminative
representations of local geometric features is unquestionably the most
important task for accurate point cloud analyses. However, it is challenging to
develop rotation or scale-invariant descriptors. Most previous studies have
either ignored rotations or empirically studied optimal scale parameters, which
hinders the applicability of the methods for real-world datasets. In this
paper, we present a new local feature description method that is robust to
rotation, density, and scale variations. Moreover, to improve representations
of the local descriptors, we propose a global aggregation method. First, we
place kernels aligned around each point in the normal direction. To avoid the
sign problem of the normal vector, we use a symmetric kernel point distribution
in the tangential plane. From each kernel point, we first projected the points
from the spatial space to the feature space, which is robust to multiple scales
and rotation, based on angles and distances. Subsequently, we perform graph
convolutions by considering local kernel point structures and long-range global
context, obtained by a global aggregation method. We experimented with our
proposed descriptors on benchmark datasets (i.e., ModelNet40 and ShapeNetPart)
to evaluate the performance of registration, classification, and part
segmentation on 3D point clouds. Our method showed superior performances when
compared to the state-of-the-art methods by reducing 70 of the rotation and
translation errors in the registration task. Our method also showed comparable
performance in the classification and part-segmentation tasks with simple and
low-dimensional architectures.Comment: 10 pages, 9 figure
PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Motivated by the success of encoding multi-scale contextual information for
image analysis, we propose our PointAtrousGraph (PAG) - a deep
permutation-invariant hierarchical encoder-decoder for efficiently exploiting
multi-scale edge features in point clouds. Our PAG is constructed by several
novel modules, such as Point Atrous Convolution (PAC), Edge-preserved Pooling
(EP) and Edge-preserved Unpooling (EU). Similar with atrous convolution, our
PAC can effectively enlarge receptive fields of filters and thus densely learn
multi-scale point features. Following the idea of non-overlapping max-pooling
operations, we propose our EP to preserve critical edge features during
subsampling. Correspondingly, our EU modules gradually recover spatial
information for edge features. In addition, we introduce chained skip
subsampling/upsampling modules that directly propagate edge features to the
final stage. Particularly, our proposed auxiliary loss functions can further
improve our performance. Experimental results show that our PAG outperform
previous state-of-the-art methods on various 3D semantic perception
applications.Comment: 11 pages, 10 figure
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