CVPR 2020 papers focusing on point cloud analysis
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D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
- [Code]
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RPM-Net: Robust Point Matching using Learned Features
- [Code]
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D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
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Cascaded Refinement Network for Point Cloud Completion
- [Code]
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PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
- [Code]
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PF-Net: Point Fractal Network for 3D Point Cloud Completion
- [Code]
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In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks
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RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
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C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds
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Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs
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Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
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- [Code]
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From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks
- [Code]
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Associate-3Ddet: Perceptual-to-Conceptual association for 3D Point Cloud Object Detection
- [Code]