KPConv: Flexible and Deformable Convolution for Point Clouds
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- Type
- preprint
- Published
- 2019-04-18
- Cited by
- 3,431
- References
- 55
- Access
- Open access
- OpenAlex
- https://openalex.org/W2938428612
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:121328056
Keywords
Kernel (algebra), Point cloud, Convolution (computer science), Computer science, Point (geometry)
References
- Multi-view Convolutional Neural Networks for 3D Shape Recognition
- Deep Residual Learning for Image Recognition
- VoxNet: A 3D Convolutional Neural Network for real-time object recognition
- Geodesic Convolutional Neural Networks on Riemannian Manifolds
- 3D Semantic Parsing of Large-Scale Indoor Spaces
- A scalable active framework for region annotation in 3D shape collections
- OctNet: Learning Deep 3D Representations at High Resolutions
- Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
- Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
- Geometric Deep Learning: Going beyond Euclidean data
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
- Deformable Convolutional Networks
- Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
- Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark
- Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
- Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- SEGCloud: Semantic Segmentation of 3D Point Clouds
- 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Cited by
- Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in CNNs
- PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames
- Generalizing discrete convolutions for unstructured point clouds
- NPTC-net: Narrow-Band Parallel Transport Convolutional Neural Networks on Point Clouds
- Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
- A review on deep learning techniques for 3D sensed data classification
- Deformable Filter Convolution for Point Cloud Reasoning
- Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data
- ConvPoint: Continuous convolutions for point cloud processing
- GMLS-Nets: A framework for learning from unstructured data
- Transformer for 3D Point Clouds
- Point Attention Network for Semantic Segmentation of 3D Point Clouds
- Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
- A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
- PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing
- Addressing the Sim2Real Gap in Robotic 3-D Object Classification
- Geometric Feedback Network for Point Cloud Classification
- SGAS: Sequential Greedy Architecture Search
- Tabulated MLP for Fast Point Feature Embedding
- Grid-GCN for Fast and Scalable Point Cloud Learning
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