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Found 5 papers, 3 papers with code
DuInNet: Dual-Modality Feature Interaction for Point Cloud Completion
Besides the fully supervised point cloud completion task, two additional tasks including denoising completion and zero-shot learning completion are proposed in ModelNet-MPC, to simulate real-world scenarios and verify the robustness to noise and the transfer ability across categories of current methods.
Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration
The adjacent points with the same category labels are then clustered together using the Euclidean clustering algorithm to obtain the semantic instances, which are represented by three kinds of attributes including spatial location information, semantic categorical information, and global geometric shape information.
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration
Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization.
3DAC: Learning Attribute Compression for Point Clouds
Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream.
Deep Learning for 3D Point Clouds: A Survey
To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.
