Papers › Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Roman Klokov, Victor Lempitsky
We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds. The new architecture performs multiplicative transformations and share parameters of these transformations according to the subdivisions of the point clouds imposed onto them by Kd-trees. Unlike the currently dominant convolutional architectures that usually require rasterization on uniform two-dimensional or three-dimensional grids, Kd-networks do not rely on such grids in any way and therefore avoid poor scaling behaviour. In a series of experiments with popular shape recognition benchmarks, Kd-networks demonstrate competitive performance in a number of shape recognition tasks such as shape classification, shape retrieval and shape part segmentation.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Part Segmentation | ShapeNet-Part | Kd-net | Class Average IoU | 77.4 | #64 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | Kd-net | Instance Average IoU | 82.3 | #64 of 67 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | Kd-Net | Overall Accuracy | 91.8 | #96 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | Kd-net | Overall Accuracy | 90.6 | #102 of 111 | Archive leaderboard | report |
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