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SOTA
3D-Semantische Segmentierung
3D Semantic Segmentation On Kitti 360
3D Semantic Segmentation On Kitti 360
Metriken
Model size
miou Val
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Model size
miou Val
Paper Title
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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