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3D Semantic Segmentation On Kitti 360
Metrics
Model size
miou Val
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||
|---|---|---|---|---|
| SuperCluster | 790K | 62.1 | Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering | |
| Superpoint Transformer | 777K | 63.5 | Efficient 3D Semantic Segmentation with Superpoint Transformer | |
| DeepViewAgg | 41.2M | 57.8 | Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation | |
| CLOUDSPAM | 37.9M | 63.6 | CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo | - |
| DA-supervised | 37.9M | 64.1 | CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo | - |
| MinkowskiNet | 37.9M | 54.2 | Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation | |
| PointNet++ | 3.0M | - | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space | |
| PointNet | N/A | - | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation |
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