Open AccessPosted Content
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
306
TL;DR: PointNet as discussed by the authors proposes a point-set kernel as a set of learnable 3D points that jointly respond to neighboring data points according to their geometric affinities measured by kernel correlation, adapted from a similar technique for point cloud registration.
read more
Abstract: Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neighborhood that contains fine-grained structural information which turns out to be helpful towards better semantic learning. In this regard, we present two new operations to improve PointNet with a more efficient exploitation of local structures. The first one focuses on local 3D geometric structures. In analogy to a convolution kernel for images, we define a point-set kernel as a set of learnable 3D points that jointly respond to a set of neighboring data points according to their geometric affinities measured by kernel correlation, adapted from a similar technique for point cloud registration. The second one exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions. Experiments show that our network can efficiently capture local information and robustly achieve better performances on major datasets. Our code is available at this http URL
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
KPConv: Flexible and Deformable Convolution for Point Clouds
Hugues Thomas,Charles R. Qi,Jean-Emmanuel Deschaud,Beatriz Marcotegui,François Goulette,Leonidas J. Guibas +5 more
- 18 Apr 2019
TL;DR: KPConv is a new design of point convolution, i.e. that operates on point clouds without any intermediate representation, that outperform state-of-the-art classification and segmentation approaches on several datasets.
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Qingyong Hu,Bo Yang,Linhai Xie,Stefano Rosa,Yulan Guo,Zhihua Wang,Niki Trigoni,Andrew Markham +7 more
- 14 Jun 2020
TL;DR: This paper introduces RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds, and introduces a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details.
•Posted Content
Dynamic Graph CNN for Learning on Point Clouds
TL;DR: In this paper, a new neural network module called EdgeConv is proposed for CNN-based high-level tasks on point clouds including classification and segmentation, which is differentiable and can be plugged into existing architectures.
1K
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Mikaela Angelina Uy,Quang-Hieu Pham,Binh-Son Hua,Thanh Nguyen,Sai-Kit Yeung +4 more
- 01 Jan 2019
TL;DR: This paper introduces ScanObjectNN, a new real-world point cloud object dataset based on scanned indoor scene data, and proposes new point cloud classification neural networks that achieve state-of-the-art performance on classifying objects with cluttered background.
•Posted Content
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
TL;DR: RS-CNN as mentioned in this paper extends regular grid CNN to irregular configuration for point cloud analysis, where the convolutional weight for local point set is forced to learn a highlevel relation expression from predefined geometric priors, between a sampled point from this point set and the others.
605
References
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
- 01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
53.5K
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner,Patrick Haffner +7 more
- 01 Jan 2001
TL;DR: This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task, and Convolutional neural networks are shown to outperform all other techniques.
32.7K
•Posted Content
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
22.7K
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
R. Qi Charles,Hao Su,Mo Kaichun,Leonidas J. Guibas +3 more
- 21 Jul 2017
TL;DR: This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
•Posted Content
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
13.1K