PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
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Summary
The Point-View Network (PVNet) is proposed, the first framework integrating both the point cloud and the multi-view data towards joint 3D shape recognition, and an embedding attention fusion scheme is proposed that could employ high-level features from the Multi-View data to model the intrinsic correlation and discriminability of different structure features fromThe point cloud data.
- Type
- article
- Published
- 2018-08-23
- Cited by
- 193
- References
- 32
- OpenAlex
- https://openalex.org/W2888486794
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:52077468
Keywords
Point cloud, Computer science, Discriminative model, Artificial intelligence, Embedding
References
- Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors
- Multi-view Convolutional Neural Networks for 3D Shape Recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- On Visual Similarity Based 3D Model Retrieval
- Camera Constraint-Free View-Based 3-D Object Retrieval
- Going deeper with convolutions
- Attention to Scale: Scale-Aware Semantic Image Segmentation
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition
- VoxNet: A 3D Convolutional Neural Network for real-time object recognition
- Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
- Volumetric and Multi-view CNNs for Object Classification on 3D Data
- Learning with Side Information through Modality Hallucination
- On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts
- DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval
- FusionNet: 3D Object Classification Using Multiple Data Representations
- Multi-View 3D Object Retrieval With Deep Embedding Network
- Multi-view 3D Object Detection Network for Autonomous Driving
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Attention-Based Multimodal Fusion for Video Description
Cited by
- MeshNet: Mesh Neural Network for 3D Shape Representation
- PVRNet: Point-View Relation Neural Network for 3D Shape Recognition
- DeepCCFV: Camera Constraint-Free Multi-View Convolutional Neural Network for 3D Object Retrieval
- A Sketch Based 3D Shape Retrieval Approach Based on Efficient Deep Point-to-Subspace Metric Learning
- Equivariant Multi-View Networks
- Effective 3-D Shape Retrieval by Integrating Traditional Descriptors and Pointwise Convolution
- PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation
- Classification of Indoor Point Clouds Using Multiviews
- Deep point-to-subspace metric learning for sketch-based 3D shape retrieval
- PointHop: An Explainable Machine Learning Method for Point Cloud Classification
- Justlookup: One Millisecond Deep Feature Extraction for Point Clouds By Lookup Tables
- SVNet: A Single View Network for 3D Shape Recognition
- Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
- PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
- Towards Robust Retrieval for Imperfectly Scanned Point Cloud Objects
- Point Attention Network for Semantic Segmentation of 3D Point Clouds
- PIEs: Pose Invariant Embeddings
- Multi-View Hierarchical Fusion Network for 3D Object Retrieval and Classification
- MMJN: Multi-Modal Joint Networks for 3D Shape Recognition
- Dual-level Embedding Alignment Network for 2D Image-Based 3D Object Retrieval
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