Tangent Convolutions for Dense Prediction in 3D

Tatarchenko, Maxim; Park, Jaesik; Koltun, Vladlen; Zhou, Qian-Yi

doi:10.1109/cvpr.2018.00409

Cited by 550 publications

(319 citation statements)

References 52 publications

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“…Le et al [17] propose to apply convolution on a regular grid with each cell containing point features that are resampled to a fixed size. Tatarchenko et al [33] perform convolution on the local tangent planes. Xie et al [41] generalize shape context to convolution for point cloud.…”

Section: Related Workmentioning

confidence: 99%

ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics

Zhang

Hua

Yeung

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

328

187

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Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data. While being able to achieve good accuracies in various scene understanding tasks, previous methods often have low training speed and complex network architecture. In this paper, we address these problems by proposing an efficient endto-end permutation invariant convolution for point cloud deep learning. Our simple yet effective convolution operator named ShellConv uses statistics from concentric spherical shells to define representative features and resolve the point order ambiguity, allowing traditional convolution to perform on such features. Based on ShellConv we further build an efficient neural network named ShellNet to directly consume the point clouds with larger receptive fields while maintaining less layers. We demonstrate the efficacy of ShellNet by producing state-of-the-art results on object classification, object part segmentation, and semantic scene segmentation while keeping the network very fast to train. Our code is publicly available in our project page 1 .

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Section: Related Workmentioning

confidence: 99%

ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics

Zhang

Hua

Yeung

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

328

187

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“…However, these approaches overlook the geometric structure in objects and scenes, especially the view-occluded 3D structures. Other methods [19,9] consider 3D object surface and apply convolutions on it for semantic analysis.…”

Section: D Representationmentioning

confidence: 99%

Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation

Jiang

Zhao

Liu³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

203

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We achieve 3D semantic scene labeling by exploring semantic relation between each point and its contextual neighbors through edges. Besides an encoder-decoder branch for predicting point labels, we construct an edge branch to hierarchically integrate point features and generate edge features. To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process. For each edge in the final graph, we predict a label to indicate the semantic consistency of the two connected points to enhance point prediction. At different layers, edge features are also fed into the corresponding point module to integrate contextual information for message passing enhancement in local regions. The two branches interact with each other and cooperate in segmentation. Decent experimental results on several 3D semantic labeling datasets demonstrate the effectiveness of our work.

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“…These structures define a neighborhood and thus convolution operations can be applied. Vice versa, specific convolutional filters can be designed for sparse 3d data [44,41].…”

Section: Related Workmentioning

confidence: 99%

Efficient Learning on Point Clouds With Basis Point Sets

Prokudin

Lassner

Romero³

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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With an increased availability of 3D scanning technology, point clouds are moving into the focus of computer vision as a rich representation of everyday scenes. However, they are hard to handle for machine learning algorithms due to their unordered structure. One common approach is to apply occupancy grid mapping, which dramatically increases the amount of data stored and at the same time loses details through discretization. Recently, deep learning models were proposed to handle point clouds directly and achieve input permutation invariance. However, these architectures often use an increased number of parameters and are computationally inefficient. In this work we propose basis point sets (BPS) as a highly efficient and fully general way to process point clouds with machine learning algorithms. The basis point set representation is a residual representation that can be computed efficiently and can be used with standard neural network architectures and other machine learning algorithms. Using the proposed representation as the input to a simple fully connected network allows us to match the performance of PointNet on a shape classification task, while using three orders of magnitude less floating point operations. In a second experiment, we show how the proposed representation can be used for registering high resolution meshes to noisy 3D scans. Here, we present the first method for single-pass high-resolution mesh registration, avoiding time-consuming per-scan optimization and allowing real-time execution.

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Tangent Convolutions for Dense Prediction in 3D

Cited by 550 publications

References 52 publications

ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics

ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics

Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation

Efficient Learning on Point Clouds With Basis Point Sets

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