8,164 research outputs found
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Point cloud analysis is very challenging, as the shape implied in irregular
points is difficult to capture. In this paper, we propose RS-CNN, namely,
Relation-Shape Convolutional Neural Network, which extends regular grid CNN to
irregular configuration for point cloud analysis. The key to RS-CNN is learning
from relation, i.e., the geometric topology constraint among points.
Specifically, the convolutional weight for local point set is forced to learn a
high-level relation expression from predefined geometric priors, between a
sampled point from this point set and the others. In this way, an inductive
local representation with explicit reasoning about the spatial layout of points
can be obtained, which leads to much shape awareness and robustness. With this
convolution as a basic operator, RS-CNN, a hierarchical architecture can be
developed to achieve contextual shape-aware learning for point cloud analysis.
Extensive experiments on challenging benchmarks across three tasks verify
RS-CNN achieves the state of the arts.Comment: Accepted to CVPR 2019 as an oral presentation. Project page at
https://yochengliu.github.io/Relation-Shape-CN
ConvPoint: Continuous Convolutions for Point Cloud Processing
Point clouds are unstructured and unordered data, as opposed to images. Thus,
most machine learning approach developed for image cannot be directly
transferred to point clouds. In this paper, we propose a generalization of
discrete convolutional neural networks (CNNs) in order to deal with point
clouds by replacing discrete kernels by continuous ones. This formulation is
simple, allows arbitrary point cloud sizes and can easily be used for designing
neural networks similarly to 2D CNNs. We present experimental results with
various architectures, highlighting the flexibility of the proposed approach.
We obtain competitive results compared to the state-of-the-art on shape
classification, part segmentation and semantic segmentation for large-scale
point clouds.Comment: 12 page
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Learning on point cloud is eagerly in demand because the point cloud is a
common type of geometric data and can aid robots to understand environments
robustly. However, the point cloud is sparse, unstructured, and unordered,
which cannot be recognized accurately by a traditional convolutional neural
network (CNN) nor a recurrent neural network (RNN). Fortunately, a graph
convolutional neural network (Graph CNN) can process sparse and unordered data.
Hence, we propose a linked dynamic graph CNN (LDGCNN) to classify and segment
point cloud directly in this paper. We remove the transformation network, link
hierarchical features from dynamic graphs, freeze feature extractor, and
retrain the classifier to increase the performance of LDGCNN. We explain our
network using theoretical analysis and visualization. Through experiments, we
show that the proposed LDGCNN achieves state-of-art performance on two standard
datasets: ModelNet40 and ShapeNet
Octree guided CNN with Spherical Kernels for 3D Point Clouds
We propose an octree guided neural network architecture and spherical
convolutional kernel for machine learning from arbitrary 3D point clouds. The
network architecture capitalizes on the sparse nature of irregular point
clouds, and hierarchically coarsens the data representation with space
partitioning. At the same time, the proposed spherical kernels systematically
quantize point neighborhoods to identify local geometric structures in the
data, while maintaining the properties of translation-invariance and asymmetry.
We specify spherical kernels with the help of network neurons that in turn are
associated with spatial locations. We exploit this association to avert dynamic
kernel generation during network training that enables efficient learning with
high resolution point clouds. The effectiveness of the proposed technique is
established on the benchmark tasks of 3D object classification and
segmentation, achieving new state-of-the-art on ShapeNet and RueMonge2014
datasets.Comment: Accepted in IEEE CVPR 2019. arXiv admin note: substantial text
overlap with arXiv:1805.0787
End-to-end 3D shape inverse rendering of different classes of objects from a single input image
In this paper a semi-supervised deep framework is proposed for the problem of
3D shape inverse rendering from a single 2D input image. The main structure of
proposed framework consists of unsupervised pre-trained components which
significantly reduce the need to labeled data for training the whole framework.
using labeled data has the advantage of achieving to accurate results without
the need to predefined assumptions about image formation process. Three main
components are used in the proposed network: an encoder which maps 2D input
image to a representation space, a 3D decoder which decodes a representation to
a 3D structure and a mapping component in order to map 2D to 3D representation.
The only part that needs label for training is the mapping part with not too
many parameters. The other components in the network can be pre-trained
unsupervised using only 2D images or 3D data in each case. The way of
reconstructing 3D shapes in the decoder component, inspired by the model based
methods for 3D reconstruction, maps a low dimensional representation to 3D
shape space with the advantage of extracting the basis vectors of shape space
from training data itself and is not restricted to a small set of examples as
used in predefined models. Therefore, the proposed framework deals directly
with coordinate values of the point cloud representation which leads to achieve
dense 3D shapes in the output. The experimental results on several benchmark
datasets of objects and human faces and comparing with recent similar methods
shows the power of proposed network in recovering more details from single 2D
images.Comment: 16 pages, 12 figures, 2 table
Dense-Resolution Network for Point Cloud Classification and Segmentation
Point cloud analysis is attracting attention from Artificial Intelligence
research since it can be widely used in applications such as robotics,
Augmented Reality, self-driving. However, it is always challenging due to
irregularities, unorderedness, and sparsity. In this article, we propose a
novel network named Dense-Resolution Network (DRNet) for point cloud analysis.
Our DRNet is designed to learn local point features from the point cloud in
different resolutions. In order to learn local point groups more effectively,
we present a novel grouping method for local neighborhood searching and an
error-minimizing module for capturing local features. In addition to validating
the network on widely used point cloud segmentation and classification
benchmarks, we also test and visualize the performance of the components.
Comparing with other state-of-the-art methods, our network shows superiority on
ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.Comment: To appear in WACV2021. Codes and models are available at:
https://github.com/ShiQiu0419/DRNe
MeshCNN: A Network with an Edge
Polygonal meshes provide an efficient representation for 3D shapes. They
explicitly capture both shape surface and topology, and leverage non-uniformity
to represent large flat regions as well as sharp, intricate features. This
non-uniformity and irregularity, however, inhibits mesh analysis efforts using
neural networks that combine convolution and pooling operations. In this paper,
we utilize the unique properties of the mesh for a direct analysis of 3D shapes
using MeshCNN, a convolutional neural network designed specifically for
triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized
convolution and pooling layers that operate on the mesh edges, by leveraging
their intrinsic geodesic connections. Convolutions are applied on edges and the
four edges of their incident triangles, and pooling is applied via an edge
collapse operation that retains surface topology, thereby, generating new mesh
connectivity for the subsequent convolutions. MeshCNN learns which edges to
collapse, thus forming a task-driven process where the network exposes and
expands the important features while discarding the redundant ones. We
demonstrate the effectiveness of our task-driven pooling on various learning
tasks applied to 3D meshes.Comment: For a two-minute explanation video see https://bit.ly/meshcnnvide
A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images
This paper focuses on the challenging task of learning 3D object surface
reconstructions from single RGB images. Existing methods achieve varying
degrees of success by using different geometric representations. However, they
all have their own drawbacks, and cannot well reconstruct those surfaces of
complex topologies. To this end, we propose in this paper a skeleton-bridged,
stage-wise learning approach to address the challenge. Our use of skeleton is
due to its nice property of topology preservation, while being of lower
complexity to learn. To learn skeleton from an input image, we design a deep
architecture whose decoder is based on a novel design of parallel streams
respectively for synthesis of curve- and surface-like skeleton points. We use
different shape representations of point cloud, volume, and mesh in our
stage-wise learning, in order to take their respective advantages. We also
propose multi-stage use of the input image to correct prediction errors that
are possibly accumulated in each stage. We conduct intensive experiments to
investigate the efficacy of our proposed approach. Qualitative and quantitative
results on representative object categories of both simple and complex
topologies demonstrate the superiority of our approach over existing ones. We
will make our ShapeNet-Skeleton dataset publicly available.Comment: 8 pages paper, 3 pages supplementary material, CVPR Oral pape
Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN
Recent advances in deep convolutional neural networks (CNNs) have motivated
researchers to adapt CNNs to directly model points in 3D point clouds. Modeling
local structure has been proven to be important for the success of
convolutional architectures, and researchers exploited the modeling of local
point sets in the feature extraction hierarchy. However, limited attention has
been paid to explicitly model the geometric structure amongst points in a local
region. To address this problem, we propose Geo-CNN, which applies a generic
convolution-like operation dubbed as GeoConv to each point and its local
neighborhood. Local geometric relationships among points are captured when
extracting edge features between the center and its neighboring points. We
first decompose the edge feature extraction process onto three orthogonal
bases, and then aggregate the extracted features based on the angles between
the edge vector and the bases. This encourages the network to preserve the
geometric structure in Euclidean space throughout the feature extraction
hierarchy. GeoConv is a generic and efficient operation that can be easily
integrated into 3D point cloud analysis pipelines for multiple applications. We
evaluate Geo-CNN on ModelNet40 and KITTI and achieve state-of-the-art
performance
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural Network
To alleviate the cost of collecting and annotating large-scale point cloud
datasets, we propose an unsupervised learning approach to learn features from
unlabeled point cloud "3D object" dataset by using part contrasting and object
clustering with deep graph neural networks (GNNs). In the contrast learning
step, all the samples in the 3D object dataset are cut into two parts and put
into a "part" dataset. Then a contrast learning GNN (ContrastNet) is trained to
verify whether two randomly sampled parts from the part dataset belong to the
same object. In the cluster learning step, the trained ContrastNet is applied
to all the samples in the original 3D object dataset to extract features, which
are used to group the samples into clusters. Then another GNN for clustering
learning (ClusterNet) is trained to predict the cluster ID of all the training
samples. The contrasting learning forces the ContrastNet to learn high-level
semantic features of objects but probably ignores low-level features, while the
ClusterNet improves the quality of learned features by being trained to
discover objects that probably belong to the same semantic categories by the
use of cluster IDs. We have conducted extensive experiments to evaluate the
proposed framework on point cloud classification tasks. The proposed
unsupervised learning approach obtained comparable performance to the
state-of-the-art unsupervised learning methods that used much more complicated
network structures. The code of this work is publicly available via:
https://github.com/lingzhang1/ContrastNet.Comment: Accepted by 3DV 201
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