MeshNet: Mesh Neural Network for 3D Shape Representation
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Summary
Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on3D shape representation.
- Type
- article
- Published
- 2018-11-28
- Cited by
- 368
- References
- 26
- Access
- Open access
- OpenAlex
- https://openalex.org/W2902078856
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53856890
Keywords
Computer science, Point cloud, Representation (politics), Computer graphics, Polygon mesh
References
- Efficient feature extraction for 2D/3D objects in mesh representation
- Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors
- Multi-view Convolutional Neural Networks for 3D Shape Recognition
- On Visual Similarity Based 3D Model Retrieval
- A symbolic method for calculating the integral properties of arbitrary nonconvex polyhedra
- Intrinsic shape context descriptors for deformable shapes
- ShapeNet: An Information-Rich 3D Model Repository
- VoxNet: A 3D Convolutional Neural Network for real-time object recognition
- Voting for Voting in Online Point Cloud Object Detection
- Volumetric and Multi-view CNNs for Object Classification on 3D Data
- Pairwise Decomposition of Image Sequences for Active Multi-view Recognition
- FusionNet: 3D Object Classification Using Multiple Data Representations
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- SO-Net: Self-Organizing Network for Point Cloud Analysis
- GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
- PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
- PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
- Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Cited by
- Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
- PointHop: An Explainable Machine Learning Method for Point Cloud Classification
- PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
- Beyond Top-Grasps Through Scene Completion
- Feature line detection of noisy triangulated CSGbased objects using deep learning
- MMJN: Multi-Modal Joint Networks for 3D Shape Recognition
- Two-Stream Network Based on Visual Saliency Sharing for 3D Model Recognition
- 3D shape recognition based on multi-modal information fusion
- Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces From 3D Intraoral Scanners
- IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning
- PolySquare: a search engine for 3D models with tag propagation
- A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications
- Self-supervised Modal and View Invariant Feature Learning
- Integrating Deep Learning into CAD/CAE System: Case Study on Road Wheel Design Automation
- Neural Pose Transfer by Spatially Adaptive Instance Normalization
- DNF-Net: A Deep Normal Filtering Network for Mesh Denoising
- Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis
- Hamming Embedding Sensitivity Guided Fusion Network for 3D Shape Representation
- Cross-modal Center Loss
- CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis
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