Proceedings Article10.1109/ICPR.2016.7900038
Point cloud labeling using 3D Convolutional Neural Network
Jing Huang,Suya You +1 more
- 01 Dec 2016
- pp 2670-2675
428
TL;DR: This paper introduces a 3D point cloud labeling scheme based on 3D Convolutional Neural Network that minimizes the prior knowledge of the labeling problem and does not require a segmentation step or hand-crafted features as most previous approaches did.
read more
Abstract: In this paper, we tackle the labeling problem for 3D point clouds. We introduce a 3D point cloud labeling scheme based on 3D Convolutional Neural Network. Our approach minimizes the prior knowledge of the labeling problem and does not require a segmentation step or hand-crafted features as most previous approaches did. Particularly, we present solutions for large data handling during the training and testing process. Experiments performed on the urban point cloud dataset containing 7 categories of objects show the robustness of our approach.
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
Deep Learning for 3D Point Clouds: A Survey
TL;DR: This paper presents a comprehensive review of recent progress in deep learning methods for point clouds, covering three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.
OctNet: Learning Deep 3D Representations at High Resolutions
Gernot Riegler,Ali Osman Ulusoy,Andreas Geiger +2 more
- 21 Jul 2017
TL;DR: The utility of the OctNet representation is demonstrated by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.
1.7K
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
Martin Simonovsky,Nikos Komodakis +1 more
- 21 Jul 2017
TL;DR: This work generalizes the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows us to handle graphs of varying size and connectivity.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Jens Behley,Martin Garbade,Andres Milioto,Jan Quenzel,Sven Behnke,Cyrill Stachniss,Juergen Gall +6 more
- 01 Oct 2019
TL;DR: In this paper, the KITTI Vision Odometry Benchmark was used to provide dense point-wise annotations for the complete 360-degree field-of-view of the employed automotive LiDAR.
•Posted Content
A Review on Deep Learning Techniques Applied to Semantic Segmentation.
Alberto Garcia-Garcia,Sergio Orts-Escolano,Sergiu Oprea,Victor Villena-Martinez,Jose Garcia-Rodriguez +4 more
TL;DR: A review on deep learning methods for semantic segmentation applied to various application areas as well as mandatory background concepts to help researchers decide which are the ones that best suit their needs and their targets.
1.4K
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
3D ShapeNets: A deep representation for volumetric shapes
Zhirong Wu,Shuran Song,Aditya Khosla,Fisher Yu,Linguang Zhang,Xiaoou Tang,Jianxiong Xiao +6 more
- 07 Jun 2015
TL;DR: This work proposes to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network, and shows that this 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.
3D Convolutional Neural Networks for Human Action Recognition
TL;DR: Wang et al. as mentioned in this paper developed a novel 3D CNN model for action recognition, which extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames.
•Proceedings Article
3D Convolutional Neural Networks for Human Action Recognition
Shuiwang Ji,Wei Xu,Ming Yang,Kai Yu +3 more
- 21 Jun 2010
TL;DR: A novel 3D CNN model for action recognition that extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames.
4.3K
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Daniel Maturana,Sebastian Scherer +1 more
- 01 Sep 2015
TL;DR: VoxNet is proposed, an architecture to tackle the problem of robust object recognition by integrating a volumetric Occupancy Grid representation with a supervised 3D Convolutional Neural Network (3D CNN).